Audit report on the Northeast Iowa Schools Insurance Trust for the year ended June 30, 2015

Audit report on the Northeast Iowa Schools Insurance Trust for the year ended June 30, 2015

Graph analysis of verbal fluency test discriminate between patients with Alzheimer’s disease, Mild Cognitive Impairment and normal elderly controls

Verbal fluency is the ability to produce a satisfying sequence of spoken words during a given time interval. The core of verbal fluency lies in the capacity to manage the executive aspects of language. The standard scores of the semantic verbal fluency test are broadly used in the neuropsychological assessment of the elderly, and different analytical methods are likely to extract even more information from the data generated in this test. Graph theory, a mathematical approach to analyze relations between items, represents a promising tool to understand a variety of neuropsychological states. This study reports a graph analysis of data generated by the semantic verbal fluency test by cognitively healthy elderly (NC), patients with Mild Cognitive Impairment – subtypes amnestic(aMCI) and amnestic multiple domain (a+mdMCI) - and patients with Alzheimer’s disease (AD). Sequences of words were represented as a speech graph in which every word corresponded to a node and temporal links between words were represented by directed edges. To characterize the structure of the data we calculated 13 speech graph attributes (SGAs). The individuals were compared when divided in three (NC – MCI – AD) and four (NC – aMCI – a+mdMCI – AD) groups. When the three groups were compared, significant differences were found in the standard measure of correct words produced, and three SGA: diameter, average shortest path, and network density. SGA sorted the elderly groups with good specificity and sensitivity. When the four groups were compared, the groups differed significantly in network density, except between the two MCI subtypes and NC and aMCI. The diameter of the network and the average shortest path were significantly different between the NC and AD, and between aMCI and AD. SGA sorted the elderly in their groups with good specificity and sensitivity, performing better than the standard score of the task. These findings provide support for a new methodological frame to assess the strength of semantic memory through the verbal fluency task, with potential to amplify the predictive power of this test. Graph analysis is likely to become clinically relevant in neurology and psychiatry, and may be particularly useful for the differential diagnosis of the elderly.

The art of building trust via e-communication channels: Post-acquisition integration as a special context

This study discusses the importance of establishing trust in post-acquisition integration context and how the use of e-channels facilitates or inhibits this process. The objective of this study is to analyze how the use of electronic communication channels influences the post-acquisition integration process in terms of trust establishment and overall integration efficiency, developing a framework as a result. Three sub-objectives are introduced: to find out the building blocks of trust in M&A’s, to analyse how the use of e-channels influence the process of trust establishment in post-acquisition integration context, and to define the consequences trust and use of e-channels have for the process. The theoretical background of the study includes literature and theories relating to trust establishment in post-acquisition integration context and how the use of e-channels influences the process of trust development on a general level. The empirical research is conducted as a single case study, based on key informant interviews. The interview data was collected between October 2015 and January 2016. Altogether nine interviews were realised; six with representatives from the acquiring firm and three with target firm members. Thematic analysis was selected as the main method for analysing and processing the qualitative data. This study finds that trust has an essential role in post-acquisition integration context, facilitating the integration process in various different ways. Hence, identifying the different building blocks of trust is important in order for members of the organisations to be better able establish and maintain trust. In today’s international business, the role of electronic communication channels has also increased in importance significantly and it was confirmed that these pose both challenges and possibilities for the development of interpersonal trust. One of the most important underlying factors influencing the trust levels via e-communication channels is the level of user’s comfort in using the different e-channels. Without sufficient and meaningful training, the communication conducted via these channels in inhibited in a number of ways. Hence, understanding the defining characteristics of e-communication together with the risks and opportunities related to the use of these can have far-reaching consequences for the post-acquisition integration process as a whole. The framework based on the findings and existing theory introduces the most central factors influencing the trust establishment together with the positive and negative consequences these have for the integration process. Moreover, organizational level consistency and the existence of shared guidelines on appropriate selection of communication channels according to the nature of the task at hand are seen as important

Partnering for bioregionalism in England: a case study of the Westcountry Rivers Trust

The adoption of bioregionalism by institutions that are instrumental in river basin management has significant potential to resolve complex water resource management problems. The Westcountry Rivers Trust (WRT) in England provides an example of how localized bioregional institutionalization of adaptive comanagement, consensus decision making, local participation, indigenous technical and social knowledge, and “win-win” outcomes can potentially lead to resilient partnership working. Our analysis of the WRT’s effectiveness in confronting nonpoint source water pollution, previously impervious to centralized agency responses, provides scope for lesson-drawing on institutional design, public engagement, and effective operation, although some evident issues remain.

Trust, confidence, and equity affect the legitimacy of natural resource governance

Social-ecological systems are often highly complex, making effective governance a considerable challenge. In large, heterogeneous systems, hierarchical institutional regimes may be efficient, but effective management outcomes are dependent on stakeholder support. This support is shaped by perceptions of legitimacy, which risks being undermined where resource users are not engaged in decision-making. Although legitimacy is demonstrably critical for effective governance, less is known about the factors contributing to stakeholders’ perceptions of legitimacy or how these perceptions are socially differentiated. We quantitatively assessed stakeholder perceptions of legitimacy (indicated by support for rules) and their contributory factors among 307 commercial fishers and tourism operators in Australia’s Great Barrier Reef Marine Park. Legitimacy was most strongly associated with trust in information from governing bodies, followed by confidence in institutional performance and the equity of management outcomes. Legitimacy differed both within and among resource user groups, which emphasizes the heterogeneous nature of commonly defined stakeholder groups. Overall, tourism operators perceived higher legitimacy than did commercial fishers, which was associated with higher trust in information from management agencies. For fishers, higher levels of trust were associated with: (1) engagement in fisheries that had high subsector cohesion and positive previous experiences of interactions with governing bodies; (2) location in areas with greater proximity to sources of knowledge, resources, and decision-making; and (3) engagement in a Reef Guardian program. These findings highlight the necessity of strategies and processes to build trust among all user groups in large social-ecological systems such as the Great Barrier Reef Marine Park. Furthermore, the social differentiation of perceptions that were observed within user groups underscores the importance of targeted strategies to engage groups that may not be heard through traditional governance channels.

Networking for conservation: social capital and perceptions of organizational success among land trust boards

As an important component in collaborative natural resource management and nonprofit governance, social capital is expected to be related to variations in the performance of land trusts. Land trusts are charitable organizations that work to conserve private land locally, regionally, or nationally. The purpose of this paper is to identify the level of structural and cognitive social capital among local land trusts, and how these two types of social capital relate to the perceived success of land trusts. The analysis integrates data for land trusts operating in the U.S. south-central Appalachian region, which includes western North Carolina, southwest Virginia, and east Tennessee. We use factor analysis to elicit different dimensions of cognitive social capital, including cooperation among board members, shared values, common norms, and communication effectiveness. Measures of structural social capital include the size and diversity of organizational networks of both land trusts and their board members. Finally, a hierarchical linear regression model is employed to estimate how cognitive and structural social capital measures, along with other organizational and individual-level attributes, relate to perceptions of land trust success, defined here as achievement of the land trusts’ mission, conservation, and financial goals. Results show that the diversity of organizational partnerships, cooperation, and shared values among land trust board members are associated with higher levels of perceived success. Organizational capacity, land trust accreditation, volunteerism, and financial support are also important factors influencing perceptions of success among local, nonprofit land trusts.

Audit report on the Office of Treasurer of State, Iowa ABLE (Achieving a Better Life Experience) Saving Plan Trust for the year ended June 30, 2016

Audit report on the Office of Treasurer of State, Iowa ABLE (Achieving a Better Life Experience) Saving Plan Trust for the year ended June 30, 2016

Citizenship education, national identity and political trust: The case of Sweden

The challenging effects of globalization upon the nation-state have been a recurrent theme in the social science discourse since the 1990’s. Nationally organized education is also seen as challenged by new demands originating from globalization. In this article it is argued that ‘nation-state’ and ‘national identity’ are highly relevant concepts when discussing a citizenship education that seeks to develop a civic ethos with, potentially, a global reach. It is further argued that the understanding of such an ethos would benefit significantly from incorporating the role of political trust since trust has been identified as a main feature of the social capital that makes democracy work. Three themes are brought together: national identity and identification, the importance for democracy of political trust and the challenges citizenship education face when carried out in a national context but intended to manage issues that go far beyond the reach of the nation-state. The importance of citizenship education is discussed using recent research on the Swedish citizenship education classroom.

Audit report on the Office of Treasurer of State, Iowa Educational Savings Plan Trust (Trust) for the year ended June 30, 2016

Audit report on the Office of Treasurer of State, Iowa Educational Savings Plan Trust (Trust) for the year ended June 30, 2016

Parallel dynamic graph-partitioning for unstructured meshes

A parallel method for the dynamic partitioning of unstructured meshes is described. The method introduces a new iterative optimisation technique known as relative gain optimisation which both balances the workload and attempts to minimise the interprocessor communications overhead. Experiments on a series of adaptively refined meshes indicate that the algorithm provides partitions of an equivalent or higher quality to static partitioners (which do not reuse the existing partition) and much more rapidly. Perhaps more importantly, the algorithm results in only a small fraction of the amount of data migration compared to the static partitioners.

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

Probabilistic Models for Scalable Knowledge Graph Construction

In the past decade, systems that extract information from millions of Internet documents have become commonplace. Knowledge graphs -- structured knowledge bases that describe entities, their attributes and the relationships between them -- are a powerful tool for understanding and organizing this vast amount of information. However, a significant obstacle to knowledge graph construction is the unreliability of the extracted information, due to noise and ambiguity in the underlying data or errors made by the extraction system and the complexity of reasoning about the dependencies between these noisy extractions. My dissertation addresses these challenges by exploiting the interdependencies between facts to improve the quality of the knowledge graph in a scalable framework. I introduce a new approach called knowledge graph identification (KGI), which resolves the entities, attributes and relationships in the knowledge graph by incorporating uncertain extractions from multiple sources, entity co-references, and ontological constraints. I define a probability distribution over possible knowledge graphs and infer the most probable knowledge graph using a combination of probabilistic and logical reasoning. Such probabilistic models are frequently dismissed due to scalability concerns, but my implementation of KGI maintains tractable performance on large problems through the use of hinge-loss Markov random fields, which have a convex inference objective. This allows the inference of large knowledge graphs using 4M facts and 20M ground constraints in 2 hours. To further scale the solution, I develop a distributed approach to the KGI problem which runs in parallel across multiple machines, reducing inference time by 90%. Finally, I extend my model to the streaming setting, where a knowledge graph is continuously updated by incorporating newly extracted facts. I devise a general approach for approximately updating inference in convex probabilistic models, and quantify the approximation error by defining and bounding inference regret for online models. Together, my work retains the attractive features of probabilistic models while providing the scalability necessary for large-scale knowledge graph construction. These models have been applied on a number of real-world knowledge graph projects, including the NELL project at Carnegie Mellon and the Google Knowledge Graph.

A graph-based hyper heuristic for timetabling problems

This paper presents an investigation of a simple generic hyper-heuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyperheuristic framework, a Tabu Search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and course timetabling problems. This underpins a multi-stage hyper-heuristic where the Tabu Search employs permutations upon a different number of graph heuristics in two stages. We study this graph-based hyper-heuristic approach within the context of exploring fundamental issues concerning the search space of the hyper-heuristic (the heuristic space) and the solution space. Such issues have not been addressed in other hyper-heuristic research. These approaches are tested on both exam and course benchmark timetabling problems and are compared with the fine-tuned bespoke state-of-the-art approaches. The results are within the range of the best results reported in the literature. The approach described here represents a significantly more generally applicable approach than the current state of the art in the literature. Future work will extend this hyper-heuristic framework by employing methodologies which are applicable on a wider range of timetabling and scheduling problems.

Hybrid Graph Heuristics within a Hyper-heuristic Approach to Exam Timetabling Problems

This paper is concerned with the hybridization of two graph coloring heuristics (Saturation Degree and Largest Degree), and their application within a hyperheuristic for exam timetabling problems. Hyper-heuristics can be seen as algorithms which intelligently select appropriate algorithms/heuristics for solving a problem. We developed a Tabu Search based hyper-heuristic to search for heuristic lists (of graph heuristics) for solving problems and investigated the heuristic lists found by employing knowledge discovery techniques. Two hybrid approaches (involving Saturation Degree and Largest Degree) including one which employs Case Based Reasoning are presented and discussed. Both the Tabu Search based hyper-heuristic and the hybrid approaches are tested on random and real-world exam timetabling problems. Experimental results are comparable with the best state-of-the-art approaches (as measured against established benchmark problems). The results also demonstrate an increased level of generality in our approach.