1,247 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    A Critical Review Of Post-Secondary Education Writing During A 21st Century Education Revolution

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    Educational materials are effective instruments which provide information and report new discoveries uncovered by researchers in specific areas of academia. Higher education, like other education institutions, rely on instructional materials to inform its practice of educating adult learners. In post-secondary education, developmental English programs are tasked with meeting the needs of dynamic populations, thus there is a continuous need for research in this area to support its changing landscape. However, the majority of scholarly thought in this area centers on K-12 reading and writing. This paucity presents a phenomenon to the post-secondary community. This research study uses a qualitative content analysis to examine peer-reviewed journals from 2003-2017, developmental online websites, and a government issued document directed toward reforming post-secondary developmental education programs. These highly relevant sources aid educators in discovering informational support to apply best practices for student success. Developmental education serves the purpose of addressing literacy gaps for students transitioning to college-level work. The findings here illuminate the dearth of material offered to developmental educators. This study suggests the field of literacy research is fragmented and highlights an apparent blind spot in scholarly literature with regard to English writing instruction. This poses a quandary for post-secondary literacy researchers in the 21st century and establishes the necessity for the literacy research community to commit future scholarship toward equipping college educators teaching writing instruction to underprepared adult learners

    LAUNDERING LOVE: A MULTI-CASE ANALYSIS OF THE EVOLUTION OF ROMANCE SCAM VICTIMS INTO CO-OFFENDING MONEY MULES

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    This thesis addresses the problems of rapidly rising cyber-enabled fraud and concomitant money laundering by focusing on romance scam victims who evolve into willing partners in money laundering schemes, known as “witting money mules.” This thesis explores how and why individuals become money mules after victimization in online romance scams. The thesis employs a grounded theory approach and investigates data from over 134,000 historical text messages between three offenders and 22 victims, as well as three participant interviews with romance scam victims. The data resulted in a grounded theory that a romantically lonely victim who persistently engages online with an offender that strategically repeats scheme-relevant premises in the guise of a romantic partner can result in the victim acceding to the offender’s exploitative requests and the eventual decision to co-offend. This theory also explains how a person can simultaneously be a victim and offender and why they would intentionally choose to help the romance scammer launder money. The literature and data similarly support a suggested definition for “grooming” in the context of romance scams. As a whole, this thesis provides insight into romance scams and money mules as a strategic pivot point that, if disrupted, can simultaneously impact a criminal organization’s ability to profit from romance scams and launder the proceeds of cyber-enabled fraud.Outstanding ThesisCivilian, Minnesota Commerce Fraud BureauApproved for public release. Distribution is unlimited

    NEMISA Digital Skills Conference (Colloquium) 2023

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    The purpose of the colloquium and events centred around the central role that data plays today as a desirable commodity that must become an important part of massifying digital skilling efforts. Governments amass even more critical data that, if leveraged, could change the way public services are delivered, and even change the social and economic fortunes of any country. Therefore, smart governments and organisations increasingly require data skills to gain insights and foresight, to secure themselves, and for improved decision making and efficiency. However, data skills are scarce, and even more challenging is the inconsistency of the associated training programs with most curated for the Science, Technology, Engineering, and Mathematics (STEM) disciplines. Nonetheless, the interdisciplinary yet agnostic nature of data means that there is opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog

    Exploring the Predictors of Indonesian Reading Literacy based on PISA Data

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    Reading achievement in Indonesia has remained low since 2000 when it first participated in PISA. In Indonesia, reading is not a specific subject, but rather an essential skill integrated with other subjects like Bahasa Indonesia, English, Social Sciences, Natural Sciences, and Mathematics, and as such, it is assessed in the PISA test. Apart from the cognitive tests, PISA also collects additional information related to schools’, teachers’, parents’, and students’ characteristics and perceptions that are related to students’ cognitive ability. Thus, the main research topics in this field are reading literacy and the factors associated with reading ability. The research study examines student and school factors and their relationship impact o student reading literacy in Indonesia, considering paper-based (PISA 2000, 2009, and follow up 2020) and computer-based reading performance (PISA 2018). A quantitative research design is used based on the research problems addressed in general that fell within the factors of reading achievement based on PISA data. This approach is used to confirm the validity and reliability of the constructs included in this study and to examine the relationships that exist among those constructs. Data collection consists of primary and secondary data collection. The study uses secondary data from PISA, as well as primary data collected in 2020 concerning the reading questionnaire and cognitive test. Secondary data from PISA 2000, 2009, and 2018 student and school questionnaires are used to examine how schools and students interrelate, which affects student achievement. The study also uses primary data collected in 2020 in a follow-up study with questionnaires adopted from PISA 2018 as the latest test with additional variables from parents and teachers. In addition to taking account of school and student factors, the results of 2020 are compared with those taken in 2000, 2009, and 2018. Thus, the longitudinal study of reading literacy based on PISA data is attempted. All constructs except the demographic items are validated using the confirmatory factor analysis (CFA) and Rasch Analysis. An analysis of all constructs that have already been anchored to the weighted likelihood estimates is conducted using structural equation modelling (SEM) and hierarchical linear modelling (HLM). To examine the factors that significantly influence students’ reading literacy in Indonesia over the four cycles, the structural equation model (with single and path analysis) and hierarchical linear model are applied. The study hypothesises that school-level factors affect the reading literacy of students. The structural equation model is used to impose a theoretical model on student variables and school variables measured by observed variables. With this model, the study explains the interrelationships between construct and observed variables. Meanwhile, a hierarchical linear model is used since the data had students who are nested in schools or students who were nested in classrooms, and classrooms are nested in schools. With this model, the study examines the effects of group variables (school- and teacher-level) and individual variables (student-level) and seeks the interaction across levels. In the analysis of the hierarchical approach, it is determined that there are consistency and nonconsistency factors towards reading literacy throughout the four cycles of analysis. There is evidence of consistent predictors at the student level in the factors of gender, reading engagement, and time spent reading. At school-level, the significant factors are: school sector in the 2000, 2018 and 2020 cycles; school location in the 2018 and 2020 cycles; ICT in the 2020 cycle; resources and technology in the 2018 cycle; assessment in the 2000 and 2018 cycles; leadership in the 2018 cycle; and school climate in the 2000 cycle. It is surprising to find that no factor was significant at the teacher-level in the 2020 cycle but a direct effect is found between teacher professional and teacher lesson activities. At student-level, the significant factors are: gender in the 2000, 2009, and 2018 cycles; the number of books in the 2000 cycle; home and educational resources in the 2018 cycle; reading engagement in the 2000, 2009, and 2018 cycles; reading diversity in the 2000, 2009, and 2018 cycles; reading online in the 2018 cycle; reading strategies in the 2009 cycle, reading confidence in the 2018 cycle, and reading time in the 2000, 2009, and 2018 cycles. The predictors are consistently available in the factor of gender, reading engagement, and reading time. In addition, the results indicate that computer-based tests (2018 cycle) provided more predictors than text-based tests (2000, 2009, and 2020 cycles). This research is particularly valuable in terms of its contributions to the theoretical, practical, and methodological aspects of reading literacy in Indonesia. This study suggests that, in general, private schools and schools located in rural or village areas require more attention regarding ICT, technology, assessment, leadership, and school climate. This likewise suggest that males should receive greater attention to reading activities, such as reading engagement and reading diversity, as well as reading states, such as reading strategies, reading confidence, and reading time. Meanwhile, females should receive more attention when it comes to online reading. Teacher professional activities plays an important role in supporting the delivery of better lessons in the classroom. In addition, it is important not to underestimate parental support in terms of the income and education of the parents. It would be beneficial for the Indonesian government in the future to maintain a curriculum based on autonomy to increase student reading achievement. Likewise, the government should include teacher and parent survey in future PISA Tests so that a more comprehensive analysis of the factors influencing reading ability can be conducted.Thesis (Ph.D.) -- University of Adelaide, School of Education, 202

    Detecting Insider Attack from Behavioral and Organizational Approach

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    With alteration in many activities to digital procedures comes vulnerability. Cyber-attack risk keeps increasing for individuals and businesses. One of the attacks that could occur inside companies or organizations is an “Insider Attack”. Due to the complexity of human factors, this issue is mainly dealt with and discussed in previous studies through a technical approach. This research aims to find the correlation between the possibility of insider attacks with behavioural and organizational factors. To evaluate the difference in practice between different business sectors in Indonesia. The data were collected through semi-structured interviews with people from diverse work backgrounds conducted online. The interview was recorded and transcribed manually. The data analysis was done using tables to help the coding and correlating variable process. This research is supposed to determine the most impactful factor based on people’s views. Possible gaps were found between theories and what happened in the practice of the company or organization. This research outcome intends to give information to future research and serve as a reference to businesses and organizations about current development and gaps in a business environment.Keywords: Digitalization Risk, Cyber Security, Cyber attack, Insider Attack, Behavioural and Organizational Factors, Gaps, Prediction, Prevention

    Comparing the Performance of Initial Coin Offerings to Crowdfunded Equity Ventures

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    Uncertainty in markets increases the likelihood of market failure due to volatility and suboptimal functioning. While initial coin offerings (ICOs) and crowdfunded equity (CFE) offerings may improve functioning in growing markets, there is a lack of knowledge and understanding pertaining to the relative efficiency and behavior of ICO markets compared to CFE markets, potentially perpetuating and thwarting the various communities they are intended to serve. The purpose of this correlational study was to compare a group of ICOs with a group of CFE offerings to identify predictive factors of funding outcomes related to both capital offering types. Efficient market hypothesis was the study’s theoretical foundation, and analysis of variance was used to answer the research question, which examined whether capital offering type predicted the amount of funds raised while controlling for access to the offering companies’ secondary control factors: historical financial data, pro forma financial projections, detailed product descriptions, video of product demonstrations, company website, company history, company leadership, and company investors. Relying on a random sample of 115 campaigns (84 ICOs and 31 CFE) from websites ICOdrops.com, localstake.com, fundable.com, and mainvest.com, results showed differences in mean funds raised between CFEs and ICOs (346,075comparedto346,075 compared to 4,756,464, respectively). ANOVA results showed no single secondary control factors and only one two-factor interaction (company leadership and company investors) influenced mean funds raised. This study may contribute to positive social change by informing best practices among market participants including entrepreneurs, regulators, scholars, and investors

    Cybersecurity Mindfulness in the Age of Mindless AIs: Investigating AI Assistants Impact in High-Reliability Organizations

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    The Focus: The focus of this Master Thesis is to investigate how AI tools, such as Large Learning Models (LLMs), impact cybersecurity operations in organizations that are regarded as highly reliable. To understand the impacts of AI tools on such operations, we also need to understand the nature of AI tools, their context of use and the experience of users that rely on them. Research Approach: This thesis is structured around two different methods of investigation. First a systematic literature review was conducted, where related articles was found in different databases, i.e. Google Scholar, Web of Science and the Basket of Eight publications. After this a Qualitative study was conducted where a multiple case study with interviews and random sampling was utilized. A total of 8 informants were interviewed for this study, each lasting ~30 minutes where the questions were based on the findings from the literature. Findings: From the literature it became clear that AIs, while better than humans in many things such as analyzing Big Data, intrusion detection and other pattern recognition activities, does bring with it many difficulties to the individual and the organization. AIs and LLMs are prone to making you develop an overreliance on them where you accept their answers because of your own biases, while the information itself might be fundamentally wrong or even deceitful. This phenomenon is called AI Hallucination and is vital to understanding an AIs effect on individuals. The literature highlighted that when using any tool, it was important to realize that the AI tool is simply a machine and might be wrong, question everything and do not accept any information at face value. Quite simply, think things through. LLMs have a problem with transparency, it is impossible to know its ‘reasoning’ behind the information it provides. This fact is supported by both the literature and the interviews themselves. Overreliance, hallucination, cultivating the wrong kind of trust and lack of transparency all lead to an individual acting mindless who takes the information as true. While they have been deceived by trusting something that essentially is untrustworthy or at the very least should have been looked more into. Implication: The practical implications for this study is that an organization, especially if it is of high reliability should carefully identify measures to avoid the negative impact of AI Assistants when used in day-to-day work in cybersecurity operations
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