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Cryptocurrency is designed for anonymous financial transactions to avoid centralized control, censorship, and regulations. To protect anonymity in the underlying P2P networking, Bitcoin adopts and supports anonymous routing of Tor, I2P, and CJDNS. We analyze the networking performances of these anonymous routing with the focus on their impacts on the blockchain consensus protocol. Compared to non-anonymous routing, anonymous routing adds inherent-by-design latency performance costs due to the additions of the artificial P2P relays. However, we discover that the lack of ecosystem plays an even bigger factor in the performances of the anonymous routing for cryptocurrency blockchain. I2P and CJDNS, both advancing the anonymous routing beyond Tor, in particular lack the ecosystem of sizable networking-peer participation. I2P and CJDNS thus result in the Bitcoin experiencing networking partitioning, which has traditionally been researched and studied in cryptocurrency/blockchain security. We focus on I2P and Tor and compare them with the non-anonymous routing because CJDNS has no active public peers resulting in no connectivity. Tor results in slow propagation while I2P yields soft partition, which is a partition effect long enough to have a substantial impact in the PoW mining. To better study and identify the latency and the ecosystem factors of the cryptocurrency networking and consensus costs, we study the behaviors both in the connection manager (directly involved in the P2P networking) and the address manager (informing the connection manager of the peer selections on the backend). This paper presents our analyses results to inform the state of cryptocurrency blockchain with anonymous routing and discusses future work directions and recommendations to resolve the performance and partition issues. 

The main objective of authentic learning is to offer students an exciting and stimulating educational setting that provides practical experiences in tackling real-world security issues. Each educational theme is composed of pre-lab, lab, and post-lab activities. Through the application of authentic learning, we create and produce portable lab equipment for AI Security and Privacy on Google CoLab. This enables students to access and practice these hands-on labs conveniently and without the need for time-consuming installations and configurations. As a result, students can concentrate more on learning concepts and gain more experience in hands-on problem-solving abilities 

Nursing homes (NHs) are critical facilities for caring frail older adults with around-the-clock formal care and personal assistance. To ensure quality of care for NH residents, an adequate staffing level is of great importance. Current NH staffing practice is mainly based on experience and regulation. The objective of this paper is to investigate the viability of experience-based and regulation-based strategies, as well as alternative staffing strategies to meet the heterogeneous service demand of NH residents at reduced labor cost under various scenarios of census compositions. We propose a predictive analytics integrated computer simulation model to characterize the heterogeneous service demand of NH residents, and further evaluate and identify promising staffing strategies at the facility level. Specifically, we propose a predictive model based on latent survival analysis to characterize diverse length-of-stay (LOS) with multiple discharge dispositions among NH residents. Further, we develop a simulation model with the incorporation of predictive analytics and domain knowledge to characterize the heterogeneous service demand of NH residents on different types of caregivers over time. Based on the simulation model, we develop a graphical user interface for the simulator to evaluate different staffing strategies at the facility level and inform NH administrators about promising strategies. We use real NH data to validate the proposed model and demonstrate its effectiveness. The proposed predictive LOS model considering multiple discharge dispositions exhibits superior prediction performance and offers better staffing decisions at reduced costs than those without the consideration. With the improved modeling fidelity via integrating predictive analytics with computer simulation, the proposed model is flexible to evaluate various staffing strategies using total labor cost as a performance metric, and can identify promising staffing strategies to meet the service demand of NH residents. Promising staffing strategies with the suggested staff-to-resident (SR) ratio can significantly reduce the total labor cost of multiple types of caregivers, as compared to the benchmark strategies, such as the SR ratios based on industrial practice or minimum requirement of state regulation. Moreover, we construct multiple scenarios of different census compositions of NH residents to demonstrate the capability of the proposed model. Our proposed model can facilitate NH staffing decision making to meet the heterogeneous service demand of NH residents at reduced labor costs. 

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research.