Strategic analysis and knowledge support systems for rural development strategies in Sub-Saharan Africa

"While greater growth in agriculture and the broader rural sector is crucial for ameliorating Africa's high levels of poverty and malnutrition, developing strategies to achieve these objectives is hindered by a number of factors, including the broad array of interventions needed, the lack of accurate data, and dearth of trained local policy analysts. As such, this paper proposes a Strategic Analysis Knowledge Support System (SAKSS) in which data, tools, and knowledge are compiled, analyzed, and disseminated for the purposes of identifying a set of priority investment and policy options to promote agricultural growth and rural development. These analyses can in turn help inform the broader process of designing, implementing, and monitoring and evaluating a country's rural development strategy. In order to be an influential and sustainable part of this process and become a genuine "knowledge system," SAKSS will need to be established with an awareness of each country s development priorities and unique political, social, and economic context. By institutionalizing SAKSS through a network structure that includes government ministries, research institutions, universities, regional organizations, non-governmental organizations, and donors, SAKSS can become not only more relevant and legitimate for its intended end-users but also help strengthen local analytical capacity to inform the policy debate on future development strategies and outcomes." Authors' AbstractAgricultural growth ,Strategic analysis ,Development policies Africa, Sub-Saharan ,

Principles of Security and Trust: 7th International Conference, POST 2018, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018, Thessaloniki, Greece, April 14-20, 2018, Proceedings

authentication; computer science; computer software selection and evaluation; cryptography; data privacy; formal logic; formal methods; formal specification; internet; privacy; program compilers; programming languages; security analysis; security systems; semantics; separation logic; software engineering; specifications; verification; world wide we

Proceedings, MSVSCC 2016

Proceedings of the 10th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2016 at VMASC in Suffolk, Virginia

Introduction to Supply Chain and Operations Management – A Real World Perspective

This project was funded by KU Libraries’ Parent’s Campaign with support from the David Shulenburger Office of Scholarly Communication & Copyright and the Open Educational Resources Working Group in the University of Kansas Libraries.This textbook looks at operations management and supply chain management from a real world perspective. The foundations of operations and supply chain are presented in a format that builds upon the theories found in most operations management texts but looks at the applications of the principles through the lens of experience and practice using examples from over 40 years as a practitioner, strategic planner and consultant. The goal of the textbook is to supplement lectures and discussions about operations and supply chain management while linking the topics to other disciplines within a business environment. The topics are grouped based on the Supply Chain Council’s Supply Chain Operations Reference Model core functions.University of Kansas Librarie

Computer Aided Verification

This open access two-volume set LNCS 13371 and 13372 constitutes the refereed proceedings of the 34rd International Conference on Computer Aided Verification, CAV 2022, which was held in Haifa, Israel, in August 2022. The 40 full papers presented together with 9 tool papers and 2 case studies were carefully reviewed and selected from 209 submissions. The papers were organized in the following topical sections: Part I: Invited papers; formal methods for probabilistic programs; formal methods for neural networks; software Verification and model checking; hyperproperties and security; formal methods for hardware, cyber-physical, and hybrid systems. Part II: Probabilistic techniques; automata and logic; deductive verification and decision procedures; machine learning; synthesis and concurrency. This is an open access book

EVALUATING ARTIFICIAL INTELLIGENCE METHODS FOR USE IN KILL CHAIN FUNCTIONS

Current naval operations require sailors to make time-critical and high-stakes decisions based on uncertain situational knowledge in dynamic operational environments. Recent tragic events have resulted in unnecessary casualties, and they represent the decision complexity involved in naval operations and specifically highlight challenges within the OODA loop (Observe, Orient, Decide, and Assess). Kill chain decisions involving the use of weapon systems are a particularly stressing category within the OODA loop—with unexpected threats that are difficult to identify with certainty, shortened decision reaction times, and lethal consequences. An effective kill chain requires the proper setup and employment of shipboard sensors; the identification and classification of unknown contacts; the analysis of contact intentions based on kinematics and intelligence; an awareness of the environment; and decision analysis and resource selection. This project explored the use of automation and artificial intelligence (AI) to improve naval kill chain decisions. The team studied naval kill chain functions and developed specific evaluation criteria for each function for determining the efficacy of specific AI methods. The team identified and studied AI methods and applied the evaluation criteria to map specific AI methods to specific kill chain functions.Civilian, Department of the NavyCivilian, Department of the NavyCivilian, Department of the NavyCaptain, United States Marine CorpsCivilian, Department of the NavyCivilian, Department of the NavyApproved for public release. Distribution is unlimited

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence