Poverty, as a phenomenon, remains an obstacle to global sustainable development. Although a universal malaise, it is more prevalent in underdeveloped countries, including Nigeria. However, because of its devastating impacts on the Nigerian economy, such as increasing death rates, high crime rates, insecurity difficulties, threats to national cohesion, and so on, successive administrations have implemented poverty alleviation programs to mitigate the consequences of this disease. Worryingly, despite a multiplicity of projects and massive human and natural resources invested to match global standards, Nigeria remains impoverished. The curiosity at how these programs fail, either because of implementation hiccups or because elites’ wealth and power influence these programs spurred the paper to assess poverty alleviation policies and elitist approaches in Nigeria. The study employed the desk study approach, as it examined secondary sources such as books, journals, articles, and magazines. Its theoretical underpinning was the elite theory. The paper discovered that several factors such as corruption, the elitist nature of the policies which in disguise reflect public interests, lack of continuity, lack of coordination and monitoring system, misappropriation of public resources, and others, led to the poor performances of government in alleviating poverty in Nigeria. The paper concludes that, while the rate of poverty index in Nigeria rises year after year, poverty alleviation efforts in Nigeria have had little or no influence on the Nigerian economy, since most of these projects are purely reflective of the elites’ interests rather than the masses. Therefore, the paper recommends that for there to be a reduction in poverty incidence in Nigeria, a holistic developmental approach should be adopted, the policies formulated and implemented should sync with the needs of the citizens, and quality and viable programs should be sustained and financed irrespective of change in government; public accountability should be instilled; proper coordination and monitoring system should be domesticated, etc.

State-owned enterprises (SOEs) manage significant portion of world economy, including in the developing countries. SOEs are expected to be active and play significant role in improving the country’s economic performance and welfare through enhancing innovation performance. However, closed innovation process and lack of collaboration hinders SOEs to reach satisfying innovation performance level. This paper explores the construction and role of innovation ecosystem in the strategic entrepreneurship process of SOEs, of which is represented by dynamic capability framework, business model innovation, and collaborative advantage. Based on the analysis, this paper concluded that the collaboration between actors in the Innovation Ecosystem (IE) has positive effect to strengthening SOE’s Sensing Capabilities (SC) related to the process of exploring and identifying innovation opportunities. The increase of Sensing Capabilities (SC) will play significant role as input or antecedent on formulating proactive Innovation Strategy (IS) in orchestrating SOE’s innovation process. SOEs which has implementing proactive Innovation Strategy (IS) will be able to build collaboration and finding right Business Model Innovation (BMI). Finally, by building collaboration with other actors through the innovative business model has significant role to increase SOE’s Collaborative Advantage (CA), which considered as a proxy for competitiveness of SOEs.

Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learning under connectionism to improve the intelligent level of cartography. This paper discusses three problems about the proposition of “deep learning + cartography”. One is the consistency between the deep learning method and the map space problem solving strategy, based on gradient descent, local correlation, feature reduction and non-linear nature that answer the feasibility of the combination of “deep learning + cartography”; the second is to analyze the challenges faced by the combination of cartography from its unique disciplinary characteristics and technical environment, involving the non-standard organization of map data, professional requirements for sample establishment, the integration of geometric and geographical features, as well as the inherent spatial scale of the map; thirdly, the entry points and specific methods for integrating map making and map application into deep learning are discussed respectively.