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The Role of Privacy within the Realm of Healthcare Wearables\u27 Acceptance and Use
Get PDFWhile there are definite benefits attributed to wearables, there are also notable risks, especially in the realm of security where personal information and/or activities are often accessible to third parties. Users weigh the benefits with the risks (Privacy Calculus) and often opt for the wearable device despite concern that their information may or will be disclosed (Privacy Paradox). While past research has focused on specific technologies, the paradox of disclosure despite concern for privacy has not been the primary focus, particularly regarding the manifestation of the paradox specific to the acceptance and use of healthcare wearables. The purpose of this research was to investigate the role of privacy regarding healthcare wearables’ acceptance and use. In that regard, a model integrating privacy with acceptance and use is proposed and tested, resulting in evidence of support for the presence of the privacy calculus and paradox
Empathizing with mHealth App Users in Application Design: Early-Stage Persona Development through Social Media Data Mining
Get PDFThe pervasiveness of mobile technologies has led to the paradigm known as mHealth. Research has shown that mHealth is useful for self-management of chronic conditions but is plagued by usability issues. Design approaches such as Design Thinking advise the use of “personas” to represent real users and aid the design of user-centered and empathy-oriented mHealth applications. The current study develops three representative personas that could inform the development of a diabetes-management mobile application using about 1.35 million social media posts that represent about 40.81% of diabetes-related conversations on Twitter. This study demonstrates that social media could be useful in human-centered design approaches by analyzing posts that offer differing perspectives on diabetes care. Further, the study shows that the characteristics of social media-inspired personas can also influence the choice of behavior theory and techniques for mHealth applications
An Overview of Identity Relationship Management in the Internet of Things
No full textIdentity and Access Management (IAM) is essential and a core of any information system in enterprise networks. IAM systems manage users on premises, focus on the needs within enterprises, and utilize static intelligence for authentication and access control. Today’s IAM requirements are much more complex due to the Internet of Things (IoT). Boundaries between home and work, on-premises and cloud, personal devices and corporate owned devices are fading away. These changes have driven the shift of IAM to Identity Relationship Management (IRM). IRM aims to meet the demands not only within enterprises, but also from the Internet. It manages not only users, but also devices. It utilizes not only static intelligence but also dynamic intelligence for authentication and access control. This paper introduces and discusses the needs of IRM for the IoT. It addresses four fundamental issues related to relationships in the IRM, i.e., what relationships need to be supported in the IRM, how relationships can be used for authentication, how relationships can be utilized for access control, and what infrastructure is required to support the IRM. The paper also differentiates IAM and IRM and points out future research directions in the IRM
The Role of Enterprise Crowdsourcing Systems on Knowledge Application
No full textOrganizations are using crowdsourcing systems to collect innovative ideas from their employees harnessing their insights of companies’ products, processes, customers, and competitors. While crowd workers in third-party crowdsourcing systems are a diverse and multifaceted population with a range of motives and experience, and yet few researchers have grappled with the facilitators of the employees’ behavior comprising the creative application of their knowledge using enterprise crowdsourcing systems (ECSs). This study develops a theoretical framework to identify ECS\u27s role and to provide the way how ECSs are related to creative behavior via knowledge sharing. The results reveal that ECS increases knowledge sharing and fully or partially effective ECS use through knowledge sharing. This could make knowledge sharing a critical factor to facilitate employees’ creative work by way of an intermediary. The findings of this study can help organization refine their ECS and creative initiatives
Non-Hazardous Industrial Solid Waste Tracking System
Get PDFThe Olmsted Non-Hazardous Industrial Solid Waste Tracking System allows waste generators of certain materials to electronically have their waste assessments evaluated, approved, and tracked through a simple online process. The current process of manually requesting evaluations, prepopulating tracking forms, and filling them out on triplicate carbonless forms is out of sync with other processes in the department. Complying with audit requirements requires pulling physical copies and providing them physically to fulfill information requests.
Waste generators in Minnesota are required to track their waste disposals for certain types of industrial waste streams. This ensures waste is accounted for at the point it is produced and is disposed of properly at a licensed facility for that waste type. Olmsted researched what other counties in Minnesota are doing to comply with the tracking and reporting requirements. In our research we found similar processes and no other county leveraging an information system to drive this process. The closest alternative we uncovered in conversation was a metro county using their document management system to capture and store images after the process was complete.
The system design started with a review of the current process and data requirements gathered and defined from the existing forms. The process was deemed to be efficient, the data structure well understood, and in compliance with the Olmsted County Industrial Solid Waste Management Plan. The project followed the Olmsted County Software Development process. The entire process utilizes an on-premise database, web server outside the Olmsted County Firewall, and Olmsted County multi-function devices on our network to capture and import physical load detail documents.
The project was successful in executing its initial goal of digitizing the process but suffered difficulties due to major shifts in key personnel as the project progressed. It is currently in production with key partners who represent waste generators and waste haulers
Analysis of Theoretical and Applied Machine Learning Models for Network Intrusion Detection
Get PDFNetwork Intrusion Detection System (IDS) devices play a crucial role in the realm of network security. These systems generate alerts for security analysts by performing signature-based and anomaly-based detection on malicious network traffic. However, there are several challenges when configuring and fine-tuning these IDS devices for high accuracy and precision. Machine learning utilizes a variety of algorithms and unique dataset input to generate models for effective classification. These machine learning techniques can be applied to IDS devices to classify and filter anomalous network traffic. This combination of machine learning and network security provides improved automated network defense by developing highly-optimized IDS models that utilize unique algorithms for enhanced intrusion detection. Machine learning models can be trained using a combination of machine learning algorithms, network intrusion datasets, and optimization techniques. This study sought to identify which variation of these parameters yielded the best-performing network intrusion detection models, measured by their accuracy, precision, recall, and F1 score metrics. Additionally, this research aimed to validate theoretical models’ metrics by applying them in a real-world environment to see if they perform as expected. This research utilized a quantitative experimental study design to organize a two-phase approach to train and test a series of machine learning models for network intrusion detection by utilizing Python scripting, the scikit-learn library, and Zeek IDS software. The first phase involved optimizing and training 105 machine learning models by testing a combination of seven machine learning algorithms, five network intrusion datasets, and three optimization methods. These 105 models were then fed into the second phase, where the models were applied in a machine learning IDS pipeline to observe how the models performed in an implemented environment. The results of this study identify which algorithms, datasets, and optimization methods generate the best-performing models for network intrusion detection. This research also showcases the need to utilize various algorithms and datasets since no individual algorithm or dataset consistently achieved high metric scores independent of other training variables. Additionally, this research also indicates that optimization during model development is highly recommended; however, there may not be a need to test for multiple optimization methods since they did not typically impact the yielded models’ overall categorization of v success or failure. Lastly, this study’s results strongly indicate that theoretical machine learning models will most likely perform significantly worse when applied in an implemented IDS ML pipeline environment. This study can be utilized by other industry professionals and research academics in the fields of information security and machine learning to generate better highly-optimized models for their work environments or experimental research
Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data
No full textBackground: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media.
Objective: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases.
Methods: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series.
Results: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days.
Conclusions: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics
