Computational intelligence based complex adaptive system-of-systems architecture evolution strategy

The dynamic planning for a system-of-systems (SoS) is a challenging endeavor. Large scale organizations and operations constantly face challenges to incorporate new systems and upgrade existing systems over a period of time under threats, constrained budget and uncertainty. It is therefore necessary for the program managers to be able to look at the future scenarios and critically assess the impact of technology and stakeholder changes. Managers and engineers are always looking for options that signify affordable acquisition selections and lessen the cycle time for early acquisition and new technology addition. This research helps in analyzing sequential decisions in an evolving SoS architecture based on the wave model through three key features namely; meta-architecture generation, architecture assessment and architecture implementation. Meta-architectures are generated using evolutionary algorithms and assessed using type II fuzzy nets. The approach can accommodate diverse stakeholder views and convert them to key performance parameters (KPP) and use them for architecture assessment. On the other hand, it is not possible to implement such architecture without persuading the systems to participate into the meta-architecture. To address this issue a negotiation model is proposed which helps the SoS manger to adapt his strategy based on system owners behavior. This work helps in capturing the varied differences in the resources required by systems to prepare for participation. The viewpoints of multiple stakeholders are aggregated to assess the overall mission effectiveness of the overarching objective. An SAR SoS example problem illustrates application of the method. Also a dynamic programing approach can be used for generating meta-architectures based on the wave model. --Abstract, page iii

Community-based health financing: CARE India's experience in the maternal and infant survival project

In a rural Indian population beset with inadequate health access due to socio-cultural and economic factors, CARE India under the Maternal and Infant Survival Project encouraged village women to form Community Based Organizations (CBOs) and to save health funds collectively. After 15 months of implementation, CBOs were formed in 345 of 447 project villages and health funds were operational in 203 villages. A total of 292 persons benefited from health funds through loans for treatment of obstetric complications and infant illnesses. Additional initiatives include social marketing, sales of disposable delivery kits, and village drug banks. Over half (56 percent) of the loans were repaid within the grace/low interest period. This experience demonstrates that village women, when appropriately encouraged, are capable of creating rules and managing health funds. The process empowers village women (through access to resources and information and the strength of social capital) to make decisions and act to improve their well being

All slums are not equal: child health conditions among the urban poor

Increasing urbanization has resulted in a faster growth of slum population. Various agencies, especially those in developing countries are finding it difficult to respond to this situation effectively. Disparities among slums exist owing to various factors. This has led to varying degrees of health burden on the slum children. Child health conditions in slums with inadequate services are worse in comparison to relatively better served slums. Identification, mapping and assessment of all slums is important for locating the hitherto missed out slums and focusing on the neediest slums. In view of the differential vulnerabilities across slums, an urban child health program should build context appropriate and community-need-responsive approaches to improve children’s health in the slums

Determinants of childhood mortality and morbidity in urban slums in India

The large and continuous increase in India's urban population and the concomitant growth of the population residing in slums has resulted in overstraining of infrastructure and deterioration in public health. The link between urbanization, a degraded environment, inaccessibility to healthcare and a deteriorating quality of life is significant and particularly evident in the sharp inequities in IMR if one looks at urban specific studies. It is hence, germane to address the appalling inequalities in the distribution and access to basic amenities and health services with a focus on enhanced service coverage, improved sanitation and water supplies and mobilization of community action for effectively mitigating the childhood death and disease burden in urban slums

Indian pediatrics - Environmental Health Project

The large and continuous increase in India's urban population and the concomitant growth of the population residing in slums has resulted in overstraining of infrastructure and deterioration in public health. The link between urbanization, a degraded environment, inaccessibility to healthcare and a deteriorating quality of life is significant and particularly evident in the sharp inequities in IMR if one looks at urban specific studies. It is hence, germane to address the appalling inequalities in the distribution and access to basic amenities and health services with a focus on enhanced service coverage, improved sanitation and water supplies and mobilization of community action for effectively mitigating the childhood death and disease burden in urban slums

Migrant Adolescent Girls in Urban Slums India: Aspirations, Opportunities and Challenges

Migrant adolescent girls in India’s fast-growing urban-slum population face multiple intersecting vulnerabilities, including gender, poverty and migrant-status. The study aims to understand the opportunities and challenges for migrant adolescent girls in low-income urban slum settings. Qualitative data were collected through interviews with girls aged 12-19 who migrated during the past two years and non-migrant adolescent girls for comparison to explore their experiences in fast-growing Indore. A groupinterview with slum women’s group members discussedways to address challenges. Push/pull factors linked with different employment/educational opportunities between rural and urban areas motivated families of unmarried girls to migrate. Recently married girls joined city-based families or accompanied husbands who were labor migrants. Neither married nor unmarried girls played decision-making roles in migration. Married migrant adolescent girls faced challenges in accessing education, employment, social opportunities and services owing to restrictions on freedom of movement, weak social networks, and little awareness of opportunities and services. Childbearing migrant girls faced particular risks. Contact with their natal families being limited, the quality of relationship with husbands and marital families was crucial for married girls’well-being. Unmarried girls attending schools were positive about the migration experience, perceiving the city to offer greater educational opportunities. Through school, they accessed opportunities for new relationships and social activities. Not all unmarried adolescent-girls wereable to access opportunities owing to family restrictions and economic circumstances. These girls’ worlds remained small despite moving to a large city. Where girls’ economic and/or family and social circumstances allowed, migration entailed a positive change that enhanced their opportunities. Specific challenges of this population segment need focus in policies and programs, prioritizing three particularly vulnerable groups: girls who are neither in education nor employment, pregnant girls or new mothers, and those with difficult relationships in marital homes. Proactive outreach to raise awareness about opportunities and services and fostering social networks through front-line workers and slum women’s groups are recommended

Reinforcement Learning (RL) Augmented Cold Start Frequency Reduction in Serverless Computing

Function-as-a-Service is a cloud computing paradigm offering an event-driven execution model to applications. It features serverless attributes by eliminating resource management responsibilities from developers and offers transparent and on-demand scalability of applications. Typical serverless applications have stringent response time and scalability requirements and therefore rely on deployed services to provide quick and fault-tolerant feedback to clients. However, the FaaS paradigm suffers from cold starts as there is a non-negligible delay associated with on-demand function initialization. This work focuses on reducing the frequency of cold starts on the platform by using Reinforcement Learning. Our approach uses Q-learning and considers metrics such as function CPU utilization, existing function instances, and response failure rate to proactively initialize functions in advance based on the expected demand. The proposed solution was implemented on Kubeless and was evaluated using a normalised real-world function demand trace with matrix multiplication as the workload. The results demonstrate a favourable performance of the RL-based agent when compared to Kubeless' default policy and function keep-alive policy by improving throughput by up to 8.81% and reducing computation load and resource wastage by up to 55% and 37%, respectively, which is a direct outcome of reduced cold starts.Comment: 13 figures, 10 pages, 3 table

EELBERT: Tiny Models through Dynamic Embeddings

We introduce EELBERT, an approach for compression of transformer-based models (e.g., BERT), with minimal impact on the accuracy of downstream tasks. This is achieved by replacing the input embedding layer of the model with dynamic, i.e. on-the-fly, embedding computations. Since the input embedding layer accounts for a significant fraction of the model size, especially for the smaller BERT variants, replacing this layer with an embedding computation function helps us reduce the model size significantly. Empirical evaluation on the GLUE benchmark shows that our BERT variants (EELBERT) suffer minimal regression compared to the traditional BERT models. Through this approach, we are able to develop our smallest model UNO-EELBERT, which achieves a GLUE score within 4% of fully trained BERT-tiny, while being 15x smaller (1.2 MB) in size.Comment: EMNLP 2023, Industry Track 9 pages, 2 figures, 5 table