We report on our experiences fielding MeetingMayhem, an interactive game that we developed, which introduces students to fundamental concepts in network security, cybersecurity, and adversarial thinking. The game is intended for students who do not necessarily have any prior background in computer science. Assuming the role of agents, two players exchange messages over a network to try to agree on a meeting time and location, while an adversary interferes with their plan. Following the Dolev-Yao model, the adversary has full control of the network: they can see all messages and modify, block, or forward them. We designed the game as a web application, where groups of three students play the game, taking turns being the adversary. The adversary is a legitimate communicant on the network, and the agents do not know who is the other agent and who is the adversary. Through gameplay, we expect students to be able to (1) identify the dangers of communicating through a computer network, (2) describe the capabilities of a Dolev-Yao adversary, and (3) apply three cryptographic primitives: symmetric encryption, asymmetric encryption, and digital signatures. We conducted surveys, focus groups, and interviews to evaluate the effectiveness of the game in achieving the learning objectives. The game helped students achieve the first two learning objectives, as well as using symmetric encryption. We found that students enjoyed playing Meeting Mayhem. We are revising MeetingMayhem to improve its user interface and to better support students to learn about asymmetric encryption and digital signatures.

OBJECTIVE: This study aimed to identify phenotypes of nutritional needs of home-visited clients with low income, and compare overall changes in knowledge, behavior, and status of nutritional needs before and after home visits by identified phenotypes. MATERIALS AND METHODS: Omaha System data collected by public health nurses from 2013 to 2018 were used in this secondary data analysis study. A total of 900 low-income clients were included in the analysis. Latent class analysis (LCA) was used to identify phenotypes of nutrition symptoms or signs. Score changes in knowledge, behavior, and status were compared by phenotype. RESULTS: The five subgroups included Unbalanced Diet, Overweight, Underweight, Hyperglycemia with Adherence, and Hyperglycemia without Adherence. Only the Unbalanced Diet and Underweight groups showed an increase in knowledge. No other changes in behavior and status were observed in any of the phenotypes. DISCUSSION AND CONCLUSIONS: This LCA using standardized Omaha System Public Health Nursing data allowed us to identify phenotypes of nutritional needs among home-visited clients with low income and prioritize nutrition areas that public health nurses may focus on as part of public health nursing interventions. The sub-optimal changes in knowledge, behavior, and status suggest a need to re-examine the intervention details by phenotype and develop strategies to tailor public health nursing interventions to effectively meet the diverse nutritional needs of home-visited clients.

Federated Learning is expected to mitigate data privacy risks but introduces more vulnerable surfaces due to its distributed nature. This paper investigates a poisoning attack on federated learning. While recent studies are actively exploring this topic in classification models of learning such as image recognition, there are few studies that address the topic in regression models. This article especially examines the impacts of poisoning attacks on the performance of load forecasting, which has hardly studied yet in academia. To this end, at first, Long Short-Term Memory is implemented on federated learning for load forecasting using publicly available energy data. The first experiment demonstrates how distributed learning affects forecasting performance. Then, we implement two poisoning attacks on the federated learning setting and run experiments to enumerate their impacts on prediction accuracy of load forecasting. Lastly, this paper proposes a spectral clustering algorithm to detect two poisoning attacks and mitigate their impacts and evaluates its performance. Experimental results demonstrate that the proposed algorithm increases forecasting accuracy by 175.9% on a sign flipping attack and by 174.8% on an additive noise attack.

In this paper, we consider the scheduling of dual-criticality sporadic task systems with arbitrary deadlines using demand bound functions. In dual-criticality systems, tasks are assigned either low-criticality or high-criticality based on assurance needs with associated worst-case execution times. Arbitrary deadlines are those that allow the deadline to be larger than the minimum separation between consecutive task instances. Demand bound functions have been used to successfully schedule dual-criticality task sets for constrained deadlines, i.e., deadlines that are always less than or equal to minimum inter-arrival separation time. We formulate a new demand bound function for a more generalized dual-criticality task system with both constrained and arbitrary deadlines on a preemptive uniprocessor.

We introduce a novel loss function, Covariance Loss, which is conceptually equivalent to conditional neural processes and has a form of regularization so that is applicable to many kinds of neural networks. With the proposed loss, mappings from input variables to target variables are highly affected by dependencies of target variables as well as mean activation and mean dependencies of input and target variables. This nature enables the resulting neural networks to become more robust to noisy observations and recapture missing dependencies from prior information. In order to show the validity of the proposed loss, we conduct extensive sets of experiments on real-world datasets with state-of-the-art models and discuss the benefits and drawbacks of the proposed Covariance Loss.

OBJECTIVE: Major issues in imaging data management of tumor response assessment in clinical trials include high human errors in data input and unstandardized data structures, warranting a new breakthrough IT solution. Thus, we aim to develop a Clinical Data Interchange Standards Consortium (CDISC)-compliant clinical trial imaging management system (CTIMS) with automatic verification and transformation modules for implementing the CDISC Study Data Tabulation Model (SDTM) in the tumor response assessment dataset of clinical trials. MATERIALS AND METHODS: In accordance with various CDISC standards guides and Response Evaluation Criteria in Solid Tumors (RECIST) guidelines, the overall system architecture of CDISC-compliant CTIMS was designed. Modules for standard-compliant electronic case report form (eCRF) to verify data conformance and transform into SDTM data format were developed by experts in diverse fields such as medical informatics, medical, and clinical trial. External validation of the CDISC-compliant CTIMS was performed by comparing it with our previous CTIMS based on real-world data and CDISC validation rules by Pinnacle 21 Community Software. RESULTS: The architecture of CDISC-compliant CTIMS included the standard-compliant eCRF module of RECIST, the automatic verification module of the input data, and the SDTM transformation module from the eCRF input data to the SDTM datasets based on CDISC Define-XML. This new system was incorporated into our previous CTIMS. External validation demonstrated that all 176 human input errors occurred in the previous CTIMS filtered by a new system yielding zero error and CDISC-compliant dataset. The verified eCRF input data were automatically transformed into the SDTM dataset, which satisfied the CDISC validation rules by Pinnacle 21 Community Software. CONCLUSIONS: To assure data consistency and high quality of the tumor response assessment data, our new CTIMS can minimize human input error by using standard-compliant eCRF with an automatic verification module and automatically transform the datasets into CDISC SDTM format.