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Multi-dimensional Security Integrity Analysis of Broad Market Internet-connected Cameras
Get PDFThis study used a quantitative approach with a cross-sectional, descriptive analysis survey design to examine the adherence of 40 internet-connected cameras against three IoT security frameworks to determine their overall security posture. Relevant literature was reviewed showing that prior studies in a similar regard had limitations, such as a small sample population, singular market segment focus, and/or a lack of validation against formalized frameworks. This study resulted in a uniform and multi-dimensional set of findings with supporting evidence, leading to a mapping against selected IoT security frameworks that was then quantitatively analyzed for their relative adherence as individual cameras, across market segments, and for the overall sample population. The resultant data provides numerous pervasive gaps in the current state of IoT security that will need to be addressed to ensure consumers are properly protected
Academic Hall of Fame
Get PDFProfessor Risë Smith started at the Karl Mundt Library in 1984 during DSU\u27s mission change from a teacher\u27s school to a regional computer and technology college. Prof. Smith immediately embraced this mission and made teaching information literacy in the computer era her career focus. She became nationally recognized for her paper, Philosophical shift: Teaching the faculty to teach information literacy, presented at the Association of College and Research Libraries 1997 conference. This article is still taught and discussed widely in library science programs across the United States. In her position in the library, Prof. Smith aided countless students and faculty members through the research process. In addition to her scholarly contributions, Prof. Smith, along with former library director Ethelle Bean, were instrumental in creating the South Dakota Library Network. As a time when most library catalogs were limited to their local materials, this network would connect the catalogs of public and academic libraries, making their materials available to anyone across the state through their local library
Managing South Dakota Alfalfa Diseases with Commercial Biological Control Agents
Get PDFAlfalfa is the fourth most valuable crop in the United States and is widely grown as feed for livestock due to its high protein content. In 2021, South Dakota harvested 1,320,000 acres of alfalfa and has some of the most acres growing in the United States. Alfalfa seedlings are highly susceptible to disease, which reduces field establishment and yield. Oomycete pathogens, Aphanomyces euteiches and Pythium sp., have devastating effects on newly seeded alfalfa fields causing seed rot and reduced root development. Current management strategies are fungicidal seed coatings and planting disease resistant alfalfa varieties. To create more of an Integrated management strategy, we investigated numerous commercial, organic biological control (biocontrol) treatments, which had previously shown to be effective against oomycetes but have not been tested against alfalfa pathogens. South Dakota and USDA isolates of both A. euteiches and Pythium sp. were evaluated against biocontrols with active ingredients such as: Streptomyces octinobacterium K61 (Mycostop), Bacillus amyloliquefaciens D747 (Southern Ag), Streptomyces lydicus WYEC 108 (Actinovate), and Bacillus subtilis QST 713 (Serenade). Biocontrol activity against A. euteiches and Pythium sp. was evaluated in growth chamber assays using seed of a susceptible alfalfa variety, Saranac. Treatment effectiveness against A. euteiches was assessed by rating seedling roots after 29 days using a standardized rating scale. Seedlings inoculated with Pythium sp. were evaluated after 5 days to calculate percent germination. Biocontrols with the active ingredient, B. amyloliquefaciens D747 have antagonistic effects against Pythium sp. increasing percent germination by up to 71%. 5. octlnobacterium K61 had high activity against several isolates of A. euteiches. Various biological control agents demonstrated activity against alfalfa pathogens, which provides growers with an additional management strategy to protect their fields. In Fall 2022, we plan on testing A. euteiches and Pythium sp. isolates against biocontrol treatments that use fungal antagonists.https://scholar.dsu.edu/erposters/1007/thumbnail.jp
A Metric For Machine Learning Vulnerability to Adversarial Examples
Get PDFMachine learning is used in myriad aspects, both in academic research and in everyday life, including safety-critical applications such as robust robotics, cybersecurity products, medial testing and diagnosis where a false positive or negative could have catastrophic results. Despite the increasing prevalence of machine learning applications and their role in critical systems we rely on daily, the security and robustness of machine learning models is still a relatively young field of research with many open questions, particularly on the defensive side of adversarial machine learning. Chief among these open questions is how best to quantify a model’s attack surface against adversarial examples. Knowing how a model will behave under attacks is critical information for personnel charged with securing critical machine learning applications, and yet research towards such an attack surface metric is incredibly sparse. This dissertation addressed this problem by using previous insights into adversarial example attacks against machine learning models as well as the properties and shortcomings of various defensive techniques to formulate a basic definition of a model’s attack surface, one which allows its behavior under adversarial example attack to be generally predicted. The proposed metric was then subjected to a limited validation using six models, three Neural Networks and three Support Vector Machines (SVMs), using three datasets consisting of random clusters of points in an x,y-coordinate plane. Models were trained against each dataset to generate versions of the same model architecture with different attack surfaces, and these versions were then subjected to attack through adversarial examples generated by a Projected Gradient Descent with Line Search (PGDLS) attack, using varying perturbation budgets for the attack to control attack strength. Model performance at each perturbation budget was recorded and analyzed, leading to a limited validation of the metric for the purpose of defining how a given model will behave against adversarial example attacks
Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging Trends, and Open Issues
Get PDFThe Internet of Vehicles (IoV), where people, fleets of electric vehicles (EVs), utility, power grids, distributed renewable energy, and communications and computing infrastructures are connected, has emerged as the next big leap in smart grids and city sectors for a sustainable society. Meanwhile, decentralized and complex grid edge faces many challenges for planning, operation, and management of power systems. Therefore, providing a reliable communications infrastructure is vital. The fourth industrial revolution, that is, a cyber-physical system in conjunction with the Internet of Things (IoT) and coexistence of edge (fog) and cloud computing brings new ways of dealing with such challenges and helps maximize the benefits of power grids. From this perspective, as a use case of IoV, we present a cloud-based EV charging framework to tackle issues of high demand in charging stations during peak hours. A price incentive scheme and another scheme, electricity supply expansion, are presented and compared with the baseline. The results demonstrate that the proposed hierarchical models improve the system performance and the quality of service (QoS) for EV customers. The proposed methods can efficiently assist system operators in managing the system design and grid stability. Further, to shed light on emerging technologies for smart and connected EVs, we elaborate on seven major trends: decentralized energy trading based on blockchain and distributed ledger technology, behavioral science and behavioral economics, artificial and computational intelligence and its applications, digital twins of IoV, software-defined IoVs, and intelligent EV charging with information-centric networking, and parking lot microgrids and EV-based virtual storage. We have also discussed some of the potential research issues in IoV to further study IoV. The integration of communications, modern power system management, EV control management, and computing technologies for IoV are crucial for grid stability and large-scale EV charging networks
PEOPLE, PROCESS, AND TECHNOLOGY IN CLINICAL DECISION SUPPORT SYSTEMS: A META-ANALYSIS
No full textArtificial Intelligence (AI) techniques assist clinicians and physicians in making more effective and well-informed decisions for their patients. Clinical decision support systems (CDSS) offer extreme promise for integrating AI into the healthcare industry. To learn and extend the work done by researchers in the past, this research presents a meta-analysis of literature reviews focusing on acceptance, adoption, avoidance, and resistance of CDSS. The investigation spanned various academic databases from January 2016 to April 2021. A conceptual model guided the classification of literature into the dimensions of People, Process, and Technology. The analysis revealed the range and evolution of research relating to CDSS and clarifies trends for practitioners and Information Systems (IS) researchers. The study concludes with recommendations to further advance CDSS acceptance, adoption, avoidance, and resistance by focusing on the People, Process, and Technology dimensions. We found that 1) technology has been identified as the main component in the studies more often than people and process and 2) adoption and acceptance have been constructed as the focus of the theoretical frameworks much more than avoidance and resistance
The Past Decade View of the IS Workforce and Gender Literature: A Systematic Review
No full textDue to the demand of Information Systems (IS) professionals, gender in the IS workforce (ISWF) has been a continuing research topic. Despite these efforts, there remains a need for a greater understanding of gender theory and an individual’s decision to pursue, succeed, and obtain promotion within the IS workforce. This research uses a systematic literature review process to critically examine the research from the last decade on gender and the ISWF. A conceptual model, ISWF Multi-Factor Model, is introduced combining IS and vocational guidance theories to categorize the focus of research identified in the systematic literature review into four areas: Individual, Workforce, Individual Influences, and Environmental Influences. The findings of this study outline the current state of gender and ISWF research and is relevant to research and practice