The CIFF Proof Procedure for Abductive Logic Programming with Constraints: Theory, Implementation and Experiments

We present the CIFF proof procedure for abductive logic programming with constraints, and we prove its correctness. CIFF is an extension of the IFF proof procedure for abductive logic programming, relaxing the original restrictions over variable quantification (allowedness conditions) and incorporating a constraint solver to deal with numerical constraints as in constraint logic programming. Finally, we describe the CIFF system, comparing it with state of the art abductive systems and answer set solvers and showing how to use it to program some applications. (To appear in Theory and Practice of Logic Programming - TPLP)

Does the Government Really Help Small Business Exporters--An Analysis of Pre-Export Financing Programs under the Small Business Expansion Act of 1980 and the Export Trading Company Act of 1982

This paper will attempt to address these questions by examining the legislation and subsequent regulations that lead to the establishment of the SBA and Eximbank guarantee programs. First, there will be a brief discussion of the types of financing needed by small-business exporters and the problems they face when they try to get financing. The 1980\u27s legislation, which attempted to redress some of those problems, as well as the regulations and guidelines that were adopted by the SBA and Eximbank to implement that legislation, will then be covered. Finally, based on an evaluation of the above regulations and guidelines, measures to streamline the two programs will be proposed

TUNING OPTIMIZATION SOFTWARE PARAMETERS FOR MIXED INTEGER PROGRAMMING PROBLEMS

The tuning of optimization software is of key interest to researchers solving mixed integer programming (MIP) problems. The efficiency of the optimization software can be greatly impacted by the solver’s parameter settings and the structure of the MIP. A designed experiment approach is used to fit a statistical model that would suggest settings of the parameters that provided the largest reduction in the primal integral metric. Tuning exemplars of six and 59 factors (parameters) of optimization software, experimentation takes place on three classes of MIPs: survivable fixed telecommunication network design, a formulation of the support vector machine with the ramp loss and L1-norm regularization, and node packing for coding theory graphs. This research presents and demonstrates a framework for tuning a portfolio of MIP instances to not only obtain good parameter settings used for future instances of the same class of MIPs, but to also gain insights into which parameters and interactions of parameters are significant for that class of MIPs. The framework is used for benchmarking of solvers with tuned parameters on a portfolio of instances. A group screening method provides a way to reduce the number of factors in a design and reduces the time it takes to perform the tuning process. Portfolio benchmarking provides performance information of optimization solvers on a class with instances of a similar structure

Using Data From Court Cases and Employee Surveys to Design Sexual Harassment Policies

Study after study shows that sexual harassment is a problem that threatens to undermine many of the inroads made by working women in the last three decades. Small business managers who fail to adopt policies  to prevent  harassment  risk losing  valuable employees, the goodwill of the public, and expensive lawsuits. Data from leading federal court cases and two landmark federal employee surveys can be used to formulate policies that prevent sexual harassment from   occurring

Human-grounded evaluations of explanation methods for text classification

Due to the black-box nature of deep learning models, methods for explaining the models’ results are crucial to gain trust from humans and support collaboration between AIs and humans. In this paper, we consider several model-agnostic and model-specific explanation methods for CNNs for text classification and conduct three human-grounded evaluations, focusing on different purposes of explanations: (1) revealing model behavior, (2) justifying model predictions, and (3) helping humans investigate uncertain predictions. The results highlight dissimilar qualities of the various explanation methods we consider and show the degree to which these methods could serve for each purpose