Sistema de movilidad: intercambiador de transporte

Fil: Vucovich, Lucía. Universidad Católica de Córdoba. Facultad de Arquitectura; Argentina

Risk Assessment in Early Software Design Based on the Software Function-Failure Design Method

Potential software failures present a sizable risk element in the design and development of many systems. In this paper, we augment the Software Function-Failure Design method, which is capable of predicting potential software failures in the very early stages of design, with the Risk in Early Design technique. This synergistic combination allows a risk assessment to be conducted at an early time in the software development process when traditional techniques are not applicable. The results are concise risk statements regarding the potential failure of functionalities with likelihood and consequence quantifications that can be used as part of a risk management program. The process is illustrated using a software failure database for the NASA Mars Exploratory Rover

FedBayes: A Zero-Trust Federated Learning Aggregation to Defend Against Adversarial Attacks

Federated learning has created a decentralized method to train a machine learning model without needing direct access to client data. The main goal of a federated learning architecture is to protect the privacy of each client while still contributing to the training of the global model. However, the main advantage of privacy in federated learning is also the easiest aspect to exploit. Without being able to see the clients' data, it is difficult to determine the quality of the data. By utilizing data poisoning methods, such as backdoor or label-flipping attacks, or by sending manipulated information about their data back to the server, malicious clients are able to corrupt the global model and degrade performance across all clients within a federation. Our novel aggregation method, FedBayes, mitigates the effect of a malicious client by calculating the probabilities of a client's model weights given to the prior model's weights using Bayesian statistics. Our results show that this approach negates the effects of malicious clients and protects the overall federation.Comment: Accepted to IEEE CCWC 202

MIA-BAD: An Approach for Enhancing Membership Inference Attack and its Mitigation with Federated Learning

The membership inference attack (MIA) is a popular paradigm for compromising the privacy of a machine learning (ML) model. MIA exploits the natural inclination of ML models to overfit upon the training data. MIAs are trained to distinguish between training and testing prediction confidence to infer membership information. Federated Learning (FL) is a privacy-preserving ML paradigm that enables multiple clients to train a unified model without disclosing their private data. In this paper, we propose an enhanced Membership Inference Attack with the Batch-wise generated Attack Dataset (MIA-BAD), a modification to the MIA approach. We investigate that the MIA is more accurate when the attack dataset is generated batch-wise. This quantitatively decreases the attack dataset while qualitatively improving it. We show how training an ML model through FL, has some distinct advantages and investigate how the threat introduced with the proposed MIA-BAD approach can be mitigated with FL approaches. Finally, we demonstrate the qualitative effects of the proposed MIA-BAD methodology by conducting extensive experiments with various target datasets, variable numbers of federated clients, and training batch sizes.Comment: 6 pages, 5 figures, Accepted to be published in ICNC 2

The Meta Salud Diabetes Implementation Study: Qualitative Methods to Assess Integration of a Health Promotion Intervention Into Primary Care to Reduce CVD Risk Among an Underserved Population With Diabetes in Sonora, Mexico

Background: Within health promotion research, there is a need to assess strategies for integration and scale up in primary care settings. Hybrid interventions that combine clinical effectiveness trials with implementation studies can elicit important contextual information on facilitators and barriers to integration within a health care system. This article describes lessons learned in developing and implementing a qualitative study of a cluster-randomized controlled trial (RCT) to reduce cardiovascular disease (CVD) among people with diabetes in Sonora, Mexico, 2015-2019. Methods:The research team worked cooperatively with health center personnel from 12 Centers that implemented the intervention. The study used observations, stakeholder meetings, case studies, staff interviews and decision maker interviews to explore issues such as staff capacity, authority, workflow, space, and conflicting priorities, as well as patients' response to the program within the clinical context and their immediate social environments. Applying a multi-layered contextual framework, two members of the research team coded an initial sample of the data to establish inclusion criteria for each contextual factor. The full team finalized definitions and identified sub nodes for the final codebook. Results: Characteristics of management, staffing, and the local environment were identified as essential to integration and eventual adoption and scale up across the health system. Issues included absence of standardized training and capacity building in chronic disease and health promotion, inadequate medical supplies, a need for program monitoring and feedback, and lack of interdisciplinary support for center staff. Lack of institutional support stemming from a curative vs. preventive approach to care was a barrier for health promotion efforts. Evolving analysis, interpretation, and discussion resulted in modifications of flexible aspects of the intervention to realities of the health center environment. Conclusion: This study illustrates that a robust and comprehensive qualitative study of contextual factors across a social ecological spectrum is critical to elucidating factors that will promote future adoption and scale up of health promotion programs in primary care. Application of conceptual frameworks and health behavior theory facilitates identification of facilitators and barriers across contexts. Trial registration: www.ClinicalTrials.gov, identifier: NCT02804698 Registered on June 17, 2016.National Institutes of Health, National Heart Lung and Blood Institute (NHLBI)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Heart Lung & Blood Institute (NHLBI) [1R01HL125996-01]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Process evaluation in the field: global learnings from seven implementation research hypertension projects in low-and middle-income countries

Background Process evaluation is increasingly recognized as an important component of effective implementation research and yet, there has been surprisingly little work to understand what constitutes best practice. Researchers use different methodologies describing causal pathways and understanding barriers and facilitators to implementation of interventions in diverse contexts and settings. We report on challenges and lessons learned from undertaking process evaluation of seven hypertension intervention trials funded through the Global Alliance of Chronic Diseases (GACD). Methods Preliminary data collected from the GACD hypertension teams in 2015 were used to inform a template for data collection. Case study themes included: (1) description of the intervention, (2) objectives of the process evaluation, (3) methods including theoretical basis, (4) main findings of the study and the process evaluation, (5) implications for the project, policy and research practice and (6) lessons for future process evaluations. The information was summarized and reported descriptively and narratively and key lessons were identified. Results The case studies were from low- and middle-income countries and Indigenous communities in Canada. They were implementation research projects with intervention arm. Six theoretical approaches were used but most comprised of mixed-methods approaches. Each of the process evaluations generated findings on whether interventions were implemented with fidelity, the extent of capacity building, contextual factors and the extent to which relationships between researchers and community impacted on intervention implementation. The most important learning was that although process evaluation is time consuming, it enhances understanding of factors affecting implementation of complex interventions. The research highlighted the need to initiate process evaluations early on in the project, to help guide design of the intervention; and the importance of effective communication between researchers responsible for trial implementation, process evaluation and outcome evaluation. Conclusion This research demonstrates the important role of process evaluation in understanding implementation process of complex interventions. This can help to highlight a broad range of system requirements such as new policies and capacity building to support implementation. Process evaluation is crucial in understanding contextual factors that may impact intervention implementation which is important in considering whether or not the intervention can be translated to other contexts

The Effects of Labor Market Pull Factors on Refugee Resettlement in the United States

Despite the budding controversy over refugees in the United States, current literature has only examined how labor market pull factors affect refugee movement within European countries. Using data from the State Department’s Refugee Processing Center and the Current Population Survey, I find that refugees are more sensitive to regional differences in unemployment rates than immigrants and natural-born Americans. A state’s unemployment rate has a statistically significant impact on the number of refugees that eventually settle in the state. Over time, refugees move to states where they are more likely to be employed, whereas immigrants and natives are less responsive to changes in regional unemployment rates. Additionally, I find that refugees do not settle in the same patterns as non-refugee immigrants or natural-born Americans. Based on these findings, prior research has failed to recognize refugees’ potential to smooth out regional labor market differences in the United States

The development of a functionality-centric approach to software early risk assessment

Three papers submitted for publication comprise this thesis. Each addresses a specific aspect of developing a functionality-centric approach to risk analysis in early software development - the Software Function-Failure Design Method (SWFFDM). This method is adapted from the electromechanical design domain for which it was developed and applied to software. It is leveraged to perform a non-subjective, early risk analysis using historical failure data and can be executed without a team of experts --Abstract, page iv