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Leaderboard Design Principles Influencing User Engagement in an Online Discussion
Get PDFAlong with the popularity of gamification, there has been increased interest in using leaderboards to promote engagement with online learning systems. The existing literature suggests that when leaderboards are designed well they have the potential to improve learning, but qualitative investigations are required in order to reveal design principles that will improve engagement. In order to address this gap, this qualitative study aims to explore students\u27 overall perceptions of popular leaderboard designs in a gamified, online discussion. Using two leaderboards reflecting performance in an online discussion, this study evaluated multiple leaderboard designs from student interviews and other data sources regarding the potential of each leaderboard to improve user engagement.
Analysis of the leaderboard designs was conducted using a single case study. The data was collected from semi-structured interviews, transcripts from the discussion data, and surveys. Interview data was analyzed using Corbin & Strauss’ (2014) open coding method. The result of the data collection was 221 minutes of recorded conversation which converted to 135 pages of transcribed text. The transcribed data was tagged with open codes, sorted, and grouped into related conceptual clusters resulting in 68 individual codes. These codes were then grouped into 16 concepts as part of the axial coding phase. The next phase of coding was theoretical or selective coding. In this phase, concepts were abstracted to eight broader categories or, in this project’s case, design principles that formed the essence of the emergent theory.
The eight categories that emerged from the data formed the essence of a theoretical model for system engagement using global, relative, and team leaderboards. The model communicates that factors which lead to positive system engagement include clear instructions, challenge/skill balance, and timely feedback. Within each of these areas are elements of the eight design principles that act as positive or negative system engagement factors.
Three significant findings were identified in the study in relation to factors influencing engagement in settings where leaderboards are used as the primary game element. First, clear instructions must include both clear goals but also a clear understanding of the system in which the leaderboard game element is employed. Second, team leaderboards must foster team accountability through the design of the leaderboard and through social influences. Data in this study demonstrated that team leaderboards which employ rankings within teams creates power social comparison on two fronts: intra competition (evaluating scores within the team) and extra (evaluating the scores among teams). Team accountability is increased as each individual’s contribution to the overall team performance is clearly seen and is reinforced via social influences of other team members and game moderators.
Finally, and most significantly, this project demonstrated that global, relative, and team leaderboards each have specific design features that create differing levels of challenge-skill balance. Global leaderboards should be redesigned to use “sliced” leaderboards to avoid negative engagement from lower ranking users. Level leaderboards should employ levels that are perceived as realistic and achievable. Team leaderboards should develop accountability with ranking of team members both between and within teams. The design decisions associated with each leaderboard are, thus, critical to ensuring optimal positive system engagement and avoiding significant negative system engagement outcomes
Social Media for Exploring Adverse Drug Events Associated with Multiple Sclerosis
Get PDFMultiple Sclerosis (MS) affects 400,000 people in the USA and almost 2.5 million people worldwide. There is no cure for MS. A variety of disease-modifying therapies are currently available. They aim to reduce disease activity that ultimately leads to disability. However, such drugs have adverse effects that vary widely among patients making the choice of a suitable drug particularly challenging. With the proliferation of social media, this research aims to understand the perspective of people with MS on social media (Twitter) in regard to Adverse Drug Events (ADEs) and to analyze ADEs as perceived by MS patients. This study helps in understanding ADEs associated with MS drugs and can further inform future medical research by highlighting and prioritizing additional clinical trials needed to better assess such adverse drug effects
Automated Post-Breach Penetration Testing through Reinforcement Learning
No full textPredicting cyber attacks to networks is ever present challenges in the security domain. Rapid growth of Artificial Intelligence (AI) has made this even more challenging as machine learning algorithms are now used to attack such systems while defense systems continue to protect them with traditional approaches. Penetration testing (pentest) has long been one way to prevent security breaches by mimicking black hat hackers to expose possible exploits and vulnerabilities. Using trained machine learning agents to automate this process is an important research area that still needs to be explored. The objective of this paper is to apply machine learning in the post-exploitation phase of penetration testing to assess the vulnerability of the system and hence, contribute to the automation process of penetration testing. We train the agent using reinforcement learning by providing an appropriate environment to explore a compromised network and find sensitive files. By utilizing several different network environments during training, we hope to generalize our agent as much as possible, allowing for more widespread application. Extended research may include training our agent for further lateral exploration and exploitation in the system
Beyond party: ideological convictions and foreign policy conflicts in the US congress
No full textRecent work finds evidence that partisan calculations, not ideological preferences, drive congressional decisions on foreign policy. While legislators support the wars launched by their party’s presidents, they often oppose those by the other party’s presidents. However, it is unclear whether such partisan calculations are limited to a narrow set of security votes or whether they are part of a broad pattern of foreign policymaking in Congress. To examine the importance of partisan versus ideological motivations, we examine the substantive contributions legislators make to, and the votes they cast on, foreign policy measures. Specifically, we collect sponsorship and voting data on amendments that allow Congress to restrict presidential spending on defense programs and foreign aid. In analyzing data from 1971 to 2016, we find that ideology is the most consistent factor that determines whether legislators propose and support spending limits on security-related bills
A Dark Web Pharma Framework for A More Efficient Investigation of Dark Web COVID-19 Vaccine Products.
Get PDFGlobally, as the COVID-19 pandemic persists, it has not just imposed a significant impact on the general well-being of individuals, exposing them to unprecedented financial hardships and online information deception. However, it has also forced consumers, buyers, and suppliers to look toward a darkened economic world – the Dark Web world – a sinister complement to the internet, driven by financial gains, where illegal goods and services are advertised sold. As the Dark Web gains an increase in recognition by normal web users during this pandemic, how to perform cybercrime investigations on the Dark Web becomes challenging for manufacturers, investigators, and law enforcement officers.
This research aims to (1) understand the Dark Web, in general, the impact Dark Web markets have on the pharmaceutical industry during the time of this pandemic, (2) comprehend the procurement of various COVID-19 vaccine products that are procured on the Dark Web, and (3) ultimately create a Dark Web pharmaceutical open-sources investigative framework, which the pharmaceutical industry, manufacturers, investigative analysts, and law enforcement can utilize. This framework will aid them in understanding better and navigating the Dark Web space as they investigate illicit activities or cyber-crimes involving COVID-19 vaccine products procured from the Dark Web markets.
The proposed framework is a methodology with four steps and was built upon the known Justine Nordine OSINT framework template, a web-based tool developed primarily in JavaScript programming language. A qualitative grounded theory analysis was applied to evaluate the tool. Research findings serve as a reference paper and contribute significantly to the pharmaceutical investigators\u27 community and the OSINT and Dark Web investigative communities
Aligning Recovery Objectives With Organizational Capabilities
Get PDFTo reduce or eliminate the impact of a cyber-attack on an organization, preparations to recover a failed system and/or data are usually made in anticipation of such an attack. To avoid a false sense of security, these preparations should, as closely as possible, reflect the organization’s capabilities, in order to inform future improvement and avoid unattainable goals. There is an absence of a strong basis for the selection of the metrics that are used to measure preparation. Informal and unreliable processes are widely used, and they often result in metrics that conflict with the organization’s capabilities and interests. The goal of this research was to establish a process that could be used to assess and validate an organization’s recovery objectives by ensuring the selection of metrics that align with the organization’s true capabilities.
To form the basis for a formalized process for selecting recovery metrics, a decision model is proposed to ensure that, at the minimum, an organization’s technical capabilities are considered, and that on the other hand, risk tolerance thresholds are not exceeded. A short survey of qualified practitioners was conducted to determine the preferred recovery metrics and other important priorities based on the expected impact of a cyber-attack. The results revealed that organizations mostly prefer to use the popular or well-known recovery objectives (RTO and RPO), and it was demonstrated that by using a clear and well-defined process, these metrics can be objectively and reliably established. Finally, considering the capabilities of an organization’s information systems, mathematical relationships between these metrics and other existing recovery metrics are proposed as part of the decision model to ensure that these recovery objectives are established within the organization’s technical and economic limits.
The resulting artifact was first evaluated using a numeric experiment to demonstrate its mathematical and technical soundness. It was then compared directly to previously proposed models using five different criteria to validate its ability to contribute meaningfully to the solution sought for the research problem. The comparison confirmed the utility, feasibility, repeatability, and reliability of the proposed solution. The artifact was then applied in a case study using an illustrative scenario comprising of real-world statistics. The findings were used to demonstrate that if a history of an information system’s performance in preparatory activities such as backup operations and recovery drills is incorporated into decisions concerning the selection of recovery objectives, the resulting metrics will more accurately represent the ability to satisfactorily recover the systems in an actual incident. This was verified by recommendations based on established frameworks. Finally, the resulting model was presented to qualified experts for expert opinion, and positive feedback was received from both technical and business operations perspectives. It was then concluded that recovery objectives can be established in alignment with the relevant details of an organization’s information systems, and that the impact on the organization’s ability to conduct recovery operations more effectively will be positive
An Application of Machine Learning to Analysis of Packed Mac Malware
Get PDFThe macOS operating system is increasingly targeted by malware. Software written for macOS, both benign and malicious, is in the Mach-O executable format. Malware authors may frustrate analysts through obfuscation methods such as packing. The field of malware research on Windows is well-established but is less so on the macOS platform. Thus far, no research has been identified that studies how machine learning can be used to detected packed Mach-O malware. This research applies supervised machine learning techniques to the classification of packed Mach-O malware. This research will answer three research questions. First, whether machine learning can classify packed Mach-O binaries. Second, whether machine learning can classify packed Mach-O malware. Third, whether machine learning can classify the family that a malware sample belongs to. A design science methodology is used to develop an artifact and apply it against the target problem. Both malware and benignware samples are collected and processed to extract useful information. This information is enriched and parsed into a feature vector. Machine learning models are trained against the three problems identified by the research questions. The model hyperparameters are tuned and relevant features are selected. The results of the experiments show that machine learning can classify packed Mach-O binaries with 100% F1 and can classify packed Mach-O malware with 94.61% F1. However, machine learning can only perform multiclass classification of packed malware family with 69.1% F1
Does Information Systems Support forCreativity Enhance Effective Information Systems Use andJob Satisfaction inVirtual Work?
No full textVirtual work has increasingly gained global popularity in the business community, with virtuality becoming integrated into traditional office work settings. As the essence of virtuality comprises geographic dispersion and information systems (IS) use, the implementation of a wide variety of novel and advanced information technologies (IT)/IS as productivity and communication tools have fueled the trend in which virtual work permeates the modern workplace. Despite the heavy use of advanced IT/IS as an integral part of virtual work, our understanding on identifying the work dynamics between IS-related antecedents and employee work outcomes in virtual work contexts is still limited. Drawing on efficacy theory, this study focuses on two important IS-related antecedents within virtual work contexts—IS support for creativity and effective IS use—and their effects on job satisfaction. Specifically, we examine the mediating effect of effective IS use on the relationship between IS support for creativity and job satisfaction above and beyond the perceived usefulness and IS satisfaction, which have previously been recognized as impactful antecedents for IS-related effectiveness. To test the posited hypotheses, data were collected (N = 504) from an online survey platform. Using multiple mediation analyses, the study results confirm our hypotheses that (1) IS support for creativity is positively related to job satisfaction in virtual work settings; and (2) effective IS use mediates the relationship between IS support for creativity and job satisfaction even after controlling for perceived usefulness and IS satisfaction, which indicates the unique explanatory power of effective IS use for increased job satisfaction in virtual work settings. Implications for theory and practice are discussed
A Design Theory for Intelligent Clinical Decision Support
No full textPoor or inadequate design of intelligent clinical decision support systems (ICDSS) can result in low adoption and use of these systems. These are some of the prevalent factors stimulating physician resistance. This resistance facilitates low physician involvement and creates a lack of trust in these systems. This is addressed through the development of a design theory for ICDSS. This is demonstrated through mapping and identifying extant literature in the context of the socio-technical model (STM). The gaps were identified through the relationships of the STM and developed into characteristics that are translated into meta-requirements informing design principles. The primary result of this research includes a design theory for ICDSS development. The developed design theory motivates and enables efficient ICDSS development, physician adoption, and more effective patient care. The design theory will also provide managers and researchers deeper insight into designing ICDSS to further improve physician adoption and use of ICDSS
Planning a secure and reliable IoT-enabled FOG-assisted computing infrastructure for healthcare
No full textTransmitting electronic medical records (EMR) and other communication in modern Internet of Things (IoT) healthcare ecosystem is both delay and integrity-sensitive. Transmitting and computing volumes of EMR data on traditional clouds away from healthcare facilities is a main source of trust-deficit using IoT-enabled applications. Reliable IoT-enabled healthcare (IoTH) applications demand careful deployment of computing and communication infrastructure (CnCI). This paper presents a FOG-assisted CnCI model for reliable healthcare facilities. Planning a secure and reliable CnCI for IoTH networks is a challenging optimization task. We proposed a novel mathematical model (i.e., integer programming) to plan FOG-assisted CnCI for IoTH networks. It considers wireless link interfacing gateways as a virtual machine (VM). An IoTH network contains three wirelessly communicating nodes: VMs, reduced computing power gateways (RCPG), and full computing power gateways (FCPG). The objective is to minimize the weighted sum of infrastructure and operational costs of the IoTH network planning. Swarm intelligence-based evolutionary approach is used to solve IoTH networks planning for superior quality solutions in a reasonable time. The discrete fireworks algorithm with three local search methods (DFWA-3-LSM) outperformed other experimented algorithms in terms of average planning cost for all experimented problem instances. The DFWA-3-LSM lowered the average planning cost by 17.31%, 17.23%, and 18.28% when compared against discrete artificial bee colony with 3 LSM (DABC-3-LSM), low-complexity biogeography-based optimization (LC-BBO), and genetic algorithm, respectively. Statistical analysis demonstrates that the performance of DFWA-3-LSM is better than other experimented algorithms. The proposed mathematical model is envisioned for secure, reliable and cost-effective EMR data manipulation and other communication in healthcare