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There is a growing need to assess and issue micro-credentials within STEM curricula. Although one approach is to insert a free-standing academic activity into the students learning and degree path, herein the development and mechanism of an alternative approach rooted in leveraging responses on digitized quiz-based assessments is developed. An online assessment and remediation protocol with accompanying Python-based toolset was developed to engage undergraduate tutors who identify and fill knowledge gaps of at-risk learners. Digitized assessments, personalized tutoring, and automated micro-credentialing scripts for Canvas LMS are used to issue skill-specific badges which motivate the learner incrementally, while increasing self-efficacy. This consisted of building upon the available Canvas LMS application programming interface to design an algorithm that takes the given Canvas LMS data to develop the automation of dispersing badges. In addition, a user centric interface was prototyped and implemented to garner high user acceptance. As well as pioneering the potential steps to efficiently migrating the classical quizzes to New Quizzes format and investigating potential steps to provide personalized YouTube video recommendations to students, based on assessment performance. Moreover, foundational research, operational objectives, and prototyping a user interface for instructor-facing micro-credentialing was established through the work represented in this document. The approach developed is shown to provide a fine-grained analysis that credentials students understanding of material from a semester-wide perspective using a scalable automation approach evaluated within the Canvas LMS.

Over the recent years, deep learning has risen in popularity due to its capabilities in learning from data and extracting features from it in an automatic manner during training. This automatic feature extraction can be a useful tool in domains which require subject-matter-experts to manually or algorithmically extract features from the data, such as in the biomedical domain. However, automatic feature extraction requires a large amount of data, which in turn makes deep learning models data-hungry. This is a challenge for adoption of deep learning to these domains which often have small amounts of training data. In this work, deep learning is implemented in the biomedical and expert-based domains in a practical manner. Through selective transfer learning, learned knowledge from other related or unrelated datasets and tasks are transferred to the target domain, alleviating the problem of low training data. Transfer learning is studied as pre-trained model transfer or off-the-shelf feature extractor transfer in expert-based domains such as drug discovery, electrocardiogram signal arrhythmia detection, and biometric recognition. The results demonstrate that deep learning's automatic feature extraction out-performs traditional expert-made features. Moreover, transfer learning stabilizes the training when low amount of data is present and enables transfer of useful knowledge and patterns to the target domain which results in better feature extraction. Having better features or higher performance in these domains can translate to real-world changes, ranging from finding a suitable drug candidate in a timely manner, to not miss-diagnosing an Electrocardiogram arrhythmia.

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As demands for memory-intensive applications continue to grow, the memory capacity of each computing node is expected to grow at a similar pace. In high-performance computing (HPC) systems, the memory capacity per compute node is decided upon the most demanding application that would likely run on such a system, and hence the average capacity per node in future HPC systems is expected to grow significantly. However, diverse applications run on HPC systems with different memory requirements and memory utilization can fluctuate widely from one application to another. Since memory modules are private for a corresponding computing node, a large percentage of the overall memory capacity will likely be underutilized, especially when there are many jobs with small memory footprints. Thus, as HPC systems are moving towards the exascale era, better utilization of memory is strongly desired. Moreover, as new memory technologies come on the market, the flexibility of upgrading memory and system updates becomes a major concern since memory modules are tightly coupled with the computing nodes. To address these issues, vendors are exploring fabric-attached memories (FAM) systems. In this type of system, resources are decoupled and are maintained independently. Such a design has driven technology providers to develop new protocols, such as cache-coherent interconnects and memory semantic fabrics, to connect various discrete resources and help users leverage advances in-memory technologies to satisfy growing memory and storage demands. Using these new protocols, FAM can be directly attached to a system interconnect and be easily integrated with a variety of processing elements (PEs). Moreover, systems that support FAM can be smoothly upgraded and allow multiple PEs to share the FAM memory pools using well-defined protocols. The sharing of FAM between PEs allows efficient data sharing, improves memory utilization, reduces cost by allowing flexible integration of different PEs and memory modules from several vendors, and makes it easier to upgrade the system. However, adopting FAM in HPC systems brings in new challenges. Since memory is disaggregated and is accessed through fabric networks, latency in accessing memory (efficiency) is a crucial concern. In addition, quality of service, security from neighbor nodes, coherency, and address translation overhead to access FAM are some of the problems that require rethinking for FAM systems. To this end, we study and discuss various challenges that need to be addressed in FAM systems. Firstly, we developed a simulating environment to mimic and analyze FAM systems. Further, we showcase our work in addressing the challenges to improve the performance and increase the feasibility of such systems; enforcing quality of service, providing page migration support, and enhancing security from malicious neighbor nodes.