Exploring an agent as an economic insider threat solution

The insider threat is a security problem that is well-known and has a long history, yet it still remains an invisible enemy. Insiders know the security processes and have accesses that allow them to easily cover their tracks. In recent years the idea of monitoring separately for these threats has come into its own. However, the tools currently in use have disadvantages and one of the most effective techniques of human review is costly. This paper explores the development of an intelligent agent that uses already in-place computing material for inference as an inexpensive monitoring tool for insider threats. Design Science Research (DSR) is a methodology used to explore and develop an IT artifact, such as for this intelligent agent research. This methodology allows for a structure that can guide a deep search method for problems that may not be possible to solve or could add to a phenomenological instantiation.

Dealing with emergent design science research projects in IS

Multiple models, methods and frameworks have been proposed to guide Design Science Research (DSR) application to address relevant classes of problems in Information Systems (IS) discipline. While much of the ambiguity around the research paradigm has been removed, only the surface has been scratched on DSR efforts where researcher takes an active role in organizational and industrial engagement to solve a specific problem and generalize the solution to a class of problems. Such DSR projects can have a significant impact on practice, link theories to real contexts and extend the scope of DSR. Considering these multiform settings, the implications to theorizing nor the crucial role of researcher in the interplay of DSR and IS projects have not been properly addressed. The emergent nature of such projects needs to be further investigated to reach such contributions for both theory and practice. This paper raises multiple theoretical, organizational and managerial considerations for a meta-level monitoring model for emergent DSR projects.

Elucidating solvent contributions to solution reactions with ab initio QM/MM methods.

Computer simulations of reaction processes in solution in general rely on the definition of a reaction coordinate and the determination of the thermodynamic changes of the system along the reaction coordinate. The reaction coordinate often is constituted of characteristic geometrical properties of the reactive solute species, while the contributions of solvent molecules are implicitly included in the thermodynamics of the solute degrees of freedoms. However, solvent dynamics can provide the driving force for the reaction process, and in such cases explicit description of the solvent contribution in the free energy of the reaction process becomes necessary. We report here a method that can be used to analyze the solvent contributions to the reaction activation free energies from the combined QM/MM minimum free-energy path simulations. The method was applied to the self-exchange S(N)2 reaction of CH(3)Cl + Cl(-), showing that the importance of solvent-solute interactions to the reaction process. The results were further discussed in the context of coupling between solvent and solute molecules in reaction processes.

A soft robust model for optimization under ambiguity

In this paper, we propose a framework for robust optimization that relaxes the standard notion of robustness by allowing the decision maker to vary the protection level in a smooth way across the uncertainty set. We apply our approach to the problem of maximizing the expected value of a payoff function when the underlying distribution is ambiguous and therefore robustness is relevant. Our primary objective is to develop this framework and relate it to the standard notion of robustness, which deals with only a single guarantee across one uncertainty set. First, we show that our approach connects closely to the theory of convex risk measures. We show that the complexity of this approach is equivalent to that of solving a small number of standard robust problems. We then investigate the conservatism benefits and downside probability guarantees implied by this approach and compare to the standard robust approach. Finally, we illustrate theme thodology on an asset allocation example consisting of historical market data over a 25-year investment horizon and find in every case we explore that relaxing standard robustness with soft robustness yields a seemingly favorable risk-return trade-off: each case results in a higher out-of-sample expected return for a relatively minor degradation of out-of-sample downside performance. © 2010 INFORMS.

Chimpanzees and bonobos distinguish between risk and ambiguity.

Although recent research has investigated animal decision-making under risk, little is known about how animals choose under conditions of ambiguity when they lack information about the available alternatives. Many models of choice behaviour assume that ambiguity does not impact decision-makers, but studies of humans suggest that people tend to be more averse to choosing ambiguous options than risky options with known probabilities. To illuminate the evolutionary roots of human economic behaviour, we examined whether our closest living relatives, chimpanzees (Pan troglodytes) and bonobos (Pan paniscus), share this bias against ambiguity. Apes chose between a certain option that reliably provided an intermediately preferred food type, and a variable option that could vary in the probability that it provided a highly preferred food type. To examine the impact of ambiguity on ape decision-making, we interspersed trials in which chimpanzees and bonobos had no knowledge about the probabilities. Both species avoided the ambiguous option compared with their choices for a risky option, indicating that ambiguity aversion is shared by humans, bonobos and chimpanzees.

Trash: a local solution to a global problem

Gemstone Team GREEN JUSTICE

Solution structure of the N-terminal transactivation domain of ERM modified by SUMO-1.

ERM is a member of the PEA3 group of the Ets transcription factor family that plays important roles in development and tumorigenesis. The PEA3s share an N-terminal transactivation domain (TADn) whose activity is inhibited by small ubiquitin-like modifier (SUMO). However, the consequences of sumoylation and its underlying molecular mechanism remain unclear. The domain structure of ERM TADn alone or modified by SUMO-1 was analyzed using small-angle X-ray scattering (SAXS). Low resolution shapes determined ab initio from the scattering data indicated an elongated shape and an unstructured conformation of TADn in solution. Covalent attachment of SUMO-1 does not perturb the structure of TADn as indicated by the linear arrangement of the SUMO moiety with respect to TADn. Thus, ERM belongs to the growing family of proteins that contain intrinsically unstructured regions. The flexible nature of TADn may be instrumental for ERM recognition and binding to diverse molecular partners.

An application of quasi-Newton methods for the numerical solution of interface problems

Quasi-Newton methods are applied to solve interface problems which arise from domain decomposition methods. These interface problems are usually sparse systems of linear or nonlinear equations. We are interested in applying these methods to systems of linear equations where we are not able or willing to calculate the Jacobian matrices as well as to systems of nonlinear equations resulting from nonlinear elliptic problems in the context of domain decomposition. Suitability for parallel implementation of these algorithms on coarse-grained parallel computers is discussed.

Fire field modelling using the SMARTFIRE automated dynamic solution control environment

SMARTFIRE is a fire field model based on an open architecture integrated CFD code and knowledge-based system. It makes use of the expert system to assist the user in setting up the problem specification and new computational techniques such as Group Solvers to reduce the computational effort involved in solving the equations. This paper concentrates on recent research into the use of artificial intelligence techniques to assist in dynamic solution control of fire scenarios being simulated using fire field modelling techniques. This is designed to improve the convergence capabilities of the software while further decreasing the computational overheads. The technique automatically controls solver relaxations using an integrated production rule engine with a blackboard to monitor and implement the required control changes during solution processing. Initial results for a two-dimensional fire simulation are presented that demonstrate the potential for considerable savings in simulation run-times when compared with control sets from various sources. Furthermore, the results demonstrate enhanced solution reliability due to obtaining acceptable convergence within each time step unlike some of the comparison simulations.

[Crossword] Solution to crossword "Not"

Tony Mann provides the solution to the crossword "Not" under the pseudonym of Parsman.

Case base adaptation using solution-space metrics

In this paper we propose a generalisation of the k-nearest neighbour (k-NN) retrieval method based on an error function using distance metrics in the solution and problem space. It is an interpolative method which is proposed to be effective for sparse case bases. The method applies equally to nominal, continuous and mixed domains, and does not depend upon an embedding n-dimensional space. In continuous Euclidean problem domains, the method is shown to be a generalisation of the Shepard's Interpolation method. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The performance of the retrieval method is examined with reference to the Iris classification problem,and to a simulated sparse nominal value test problem. The introducion of a solution-space metric is shown to out-perform conventional nearest neighbours methods on sparse case bases.

Case base reduction using solution-space metrics

In this paper we propose a case base reduction technique which uses a metric defined on the solution space. The technique utilises the Generalised Shepard Nearest Neighbour (GSNN) algorithm to estimate nominal or real valued solutions in case bases with solution space metrics. An overview of GSNN and a generalised reduction technique, which subsumes some existing decremental methods, such as the Shrink algorithm, are presented. The reduction technique is given for case bases in terms of a measure of the importance of each case to the predictive power of the case base. A trial test is performed on two case bases of different kinds, with several metrics proposed in the solution space. The tests show that GSNN can out-perform standard nearest neighbour methods on this set. Further test results show that a caseremoval order proposed based on a GSNN error function can produce a sparse case base with good predictive power.

[Crossword] Crossword solution - by Parsman

Tony Mann provides the crossword solution under the pseudonym of Parsman.

Parallel algorithms of the Purcell method for direct solution of linear systems

In this paper, we first demonstrate that the classical Purcell's vector method when combined with row pivoting yields a consistently small growth factor in comparison to the well-known Gauss elimination method, the Gauss–Jordan method and the Gauss–Huard method with partial pivoting. We then present six parallel algorithms of the Purcell method that may be used for direct solution of linear systems. The algorithms differ in ways of pivoting and load balancing. We recommend algorithms V and VI for their reliability and algorithms III and IV for good load balance if local pivoting is acceptable. Some numerical results are presented.