Managing and Securing Business Networks in the Smartphone Era

This paper discusses the impact of user owned mobile computing devices (smartphones, tablets, and future devices like Google Glass) on management and security of the corporate network. Personally owned portable computing devices are widely used at work and create a porous network perimeter for the enterprise network. The paper reviews corporate policies posted on websites along with research papers and corporate whitepapers to develop a comprehensive user owned mobile computing device policy. This is a rapidly evolving topic that has not been researched in the business academic literature. We survey trade journals and corporate websites for information regarding this policy and make recommendations that can be applied by business managers

Managing and Securing Business Networks in the Smartphone Era

This paper discusses the impact of user owned mobile computing devices (smartphones, tablets, and future devices like Google Glass) on management and security of the corporate network. Personally owned portable computing devices are widely used at work and create a porous network perimeter for the enterprise network. The paper reviews corporate policies posted on websites along with research papers and corporate whitepapers to develop a comprehensive user owned mobile computing device policy. This is a rapidly evolving topic that has not been researched in the business academic literature. We survey trade journals and corporate websites for information regarding this policy and make recommendations that can be applied by business managers

SCENARIO PROBABILITY ELICITATION PROCEDURES (DELPHI)

Scenario probabilities elicited from experts are useful in decision making under uncertain future conditions, especially when a reliable and sufficient source of past data is unavailable. Various approaches have been proposed, and used, to simplify the determination of scenario probabilities. These include micro level approaches based on the analysis of relevant underlying events and their interrelations, and direct macro level examination of the scenarios. The determination of a unique solution demands excessive consistency and time requirements on the part of the expert and is often not guaranteed by many of these procedures. The procedure described in this thesis is designed to elicit cognitively simple and reliable information from experts in order to determine the probabilities of various possible scenarios. Queries presented to the expert are selected at each step, to maximize the expected information content of the expert\u27s response. A procedure to select the queries which provide the most information, and generate a partial ranking for the scenario probabilities in the presence of inconsistent ranking responses is developed. The services of either a single expert of a panel of judges can be utilized. The Delphi procedure is explained through a model based on catastrophe theory. It is also demonstrated that splitting up the query process for the conditional probability range estimates, and the scenario probability ranks, is desirable when the opinions of a group of experts are obtained. A combined Delphi and filtering approach for determining a group consensus is described. A theoretically valid procedure to break up a large problem, and use separate groups of experts to estimate the scenarios in the different subsets is proposed. A procedure is proposed to evaluate the immunity, or lack of susceptibility, of a decision tree to unforeseen events using some elementary assumptions about the effects of such events and their probabilities. This results in the estimation of a surprise factor which provides a measure of the need for considering the possibility of surprises in reaching an optimal decision. A computer program has been written to implement this information maximizing query procedure (IMQP)

Exploratory Study Using Machine Learning to make Early Predictions of Student Outcomes

Predicting student outcomes early in a learning series can allow for changes in the learning activities to adapt to learner needs and improve outcomes. However, in instructor lead activities, instructors are often faced with a large number of learners and little readily available data on student progress. Particularly, in the case of MOOC\u27s, student data can overwhelm manual human interpretation. Further, in computer driven tutorials, systems have little ability to adapt to students behaviors. This paper reports on an exploratory study of a machine learning system that predicts student grades based on the combination of behavioral and traditional data

Modeling auditor utility functions

Utility functions for profits and losses are obtained on 14 practicing auditors. Data were elicited through personal interviews with audit partners and seniors. The appropriate utility functions that describe the risk characteristics of the auditors and fit the certainty equivalent responses are found using a general summed exponential function and nonlinear programming to obtain its parameters. The richness of the summed exponential in assuming a variety of utility shapes and its mathematical tractability, for example, in manipulating models makes it attractive for expressing an individual's utility function. Use of the summed exponential mitigates the difficulties in assessing a utility function by simplifying the assessments and circumvents the need for the analyst to decide on the appropriate functional form. The empirical results show, for example, that a firm's seniors are less averse to losses than its partners. With respect to gains, a variety of utility shapes/risk preferences are exhibited with most being risk averse for large sums of money. The implications for an auditing firm are then discussed.

Simplified Assessment of Single and Multi-Attribute Utility Functions

Two mathematical programs are presented that support the gathering and analysis of data that could be applied in a decision-making software that would more accurately portray the decision-maker\u27s values