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This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

Californian Bessie Condos Egan relates the fascinating story of children and youth services and family literacy in the libraries of the nation's most complex and multicultural state. By exploring the demographical breakup of California, Egan outlines the problems that come along with rapid growth along with the areas that need attention in regards to children's literacy services.

The purpose of this paper is to explore the barriers to coordination in humanitarian supply chain management (HSCM), proposes solutions and prioritizes them to overcome the barriers particularly in the Indian context.

This study adopts a comprehensive and rigorous procedure to explore the barriers and solutions to coordination in HSCM. The research design is divided into three phases; first, the barriers and solutions are collected through an extensive literature review; second, barriers and solutions were verified with experts involved in relief operations of the disaster that occurred in Uttarakhand (a Northern state in India) on June 14, 2013 and finally, based on the weight of barriers estimated by fuzzy analytic hierarchy process, solutions to overcome the barriers are prioritized using fuzzy technique for order performance by similarity to ideal solution that considers uncertainty and impreciseness rather than a crisp value.

This study explored 23 barriers to coordination in HSCM and grouped into five categories i.e., strategic barriers, individual barriers, organizational barriers, technological barriers and cultural barriers, and finally 15 solutions were proposed and prioritized to overcome the barriers so decision makers can focus on overcoming these barriers and realize the benefits of coordination in HSCM.

This study provides a more efficient, effective, robust and systematic way to overcome barriers to coordination and improve the competencies of humanitarian supply chain (HSC).

This is the first kind of study that prioritizes the solutions to enhance coordination in HSC based on the weight of the barriers.

This paper aims to focus on the different types of insider-led cyber frauds that gained mainstream attention in recent large-scale fraud events involving prominent Indian banking institutions. In addition to identifying and classifying cyber fraud, the study maps them on a severity scale for optimal mitigation planning.

The methodology used for identification and classification is an analysis of a detailed literature review, a focus group discussion with risk and vigilance officers and cyber cell experts, as well as secondary data of cyber fraud losses. Through machine learning-based random forest, the authors predicted the future of insider-led cyber frauds in the Indian banking business and prioritized and predicted the same. The projected future reveals the dominance of a few specific cyber frauds, which will make it easier to develop a fraud mitigation model based on a victim-centric approach.

The paper concludes with a conceptual framework that can be used to ensure a sustainable cyber fraud mitigation ecosystem within the scope of the study. By using the findings of this research, policymakers and fraud investigators will be able to create a more robust environment for banks through timely detection of cyber fraud and prevent it appropriately before it happens.

The study focuses on fraud, risk and mitigation from a victim-centric perspective and does not address it from the fraudster’s perspective. Data availability was a challenge. Banks are recommended to compile data that can be used for analysis both by themselves and other policymakers.

The structured, sustainable cyber fraud mitigation suggested in the study will provide an agile, quick, proactive, stakeholder-specific plan that helps to safeguard banks, employees, regulatory authorities, customers and the economy. It saves resources, cost and time for bank authorities and policymakers. The mitigation measures will also help improve the reputational status of the Indian banking business and prolong the banks’ sustenance.

The innovative cyber fraud mitigation approach contributes to the sustainability of a bank’s ecosystem quickly, proactively and effectively.