bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Audit Framework for Expert Systems
Authors: McKee Thomas E. 
Published: March 2014

AbstractExpert systems moved from research laboratories to widespread use in the business world during the previous decade. Current data indicate that use will continue to increase in the foreseeable future. However, auditors have not comprehensively addressed the need for and feasibility of independent audits of these systems. This paper discusses factors that will soon create a demand for independent audits of expert systems. It then identifies a framework present in current professional standards that appears to be appropriate for proceeding with the auditing of expert systems. A hypothetical case is subsequently presented to illustrate one possibility of how measurement criteria, audit procedures and an audit report developed under these professional standards might appear. Potential practice problems and research issues are identified throughout the paper.
A Comparative Analysis of Inductive‐Learning Algorithms
Authors: Chung Hyung‐Min Michael , Tam Kar Yan 
Published: March 2014

AbstractRecently there has been an increasing interest in applying inductive learning algorithms to generate rules/patterns from a given example set. While such approaches serve as an efficient way of resolving the knowledge‐acquisition bottleneck, their predictive accuracy, which is the popular measure of performance, varies widely. This paper contrasts major inductive‐learning algorithms and examines their performance with two performance measures: the predictive accuracy and the representation language. Experiments involved three inductive‐learning algorithms and five different managerial tasks in construction project assessment and bankruptcy‐prediction domains. The test results indicate that the model performance is dependent on tasks with an exception of the neural network model and that there is a an effect of group proportion in the example set used to construct the model. The neural network approach presents relatively stable predictive power across different task domains, although it is difficult to interpret its representation.
Proactive or Reactive: An Analysis of the Effect of Agent Style on Organizational Decision‐making Performance
Authors: Lin Zhiang , Carley Kathleen 
Published: March 2014

AbstractThis paper addresses the issue of agent style—proactive and reactive—from a theoretical perspective. The results show that agent style, though often considered key in decision making, only affects the organization's performance when the organization is under moderate time pressure. Further, the effect of agent style depends on the type of training given to organizational agents and the internal condition under which the organization operates. This research suggests that, when resources are scarce, organizations should spend these resources on organizational design and on increasing the accuracy of incoming information rather than on altering the agents' style.
Recognizing and Revising Unconvincing Explanations
Authors: Quilici Alex 
Published: March 2014

AbstractExpert system explanations sometimes fail to satisfy their users. The standard assumption is that these failures arise because the user did not completely understand the system's explanation. However, users may also be left unsatisfied because they are unconvinced by the explanation provided. Advice‐giving systems should therefore eventually be extended to recognize when and why an explanation has failed to convince the user and to elaborate or revise explanations to make them more convincing. This paper provides an initial model of this process for systems that give advice about choosing between multiple alternatives for accomplishing a particular task. In particular, we discuss the model underlying a prototype system to advise novice computer users about how best to accomplish tasks on the UNIX operating system.
Scheduling Shipbuilding Using a Constraint Directed Graph Search: DAS‐ERECT
Authors: Lee Jae Kyu , Choi Hyung Rim , Yang Ok Ryul , Kim Ho Dong 
Published: March 2014

AbstractTo build a scheduling system for shipbuilding, we have adopted a hierarchical architecture and developed a constraint directed graph search technique for erection scheduling at the dock. First, in the hierarchical architecture, detailed schedules for the lower‐level assembly plants are delegated to the individual plant's schedulers as long as the requirements from the higher‐level scheduler are satisfied. However, if lower‐level scheduling is impossible, the higher‐level scheduler attempts to adjust the original requirements. Second, in the constraint directed graph search, the concepts of graph expansion and constraint directed pruning are amalgamated into an algorithm. The approaches are implemented in the DAS‐ERECT system which is a sub‐system of the DAS project undertaken for the Daewoo Shipbuilding Company.
Unifying Rule Induction and Rule Refinement—Towards Discovering Anomaly from Granville's Law in a Stock Market Technical Analysis
Authors: Yamaguchi Takahira , Tachibana Yoshiaki 
Published: March 2014

AbstractThis paper discusses a framework for refining an initial object‐level rule base with a rule induction to learn meta‐level rules which find a data set applicable to an object‐level rule. A rule induction process such as ID3 tries to learn meta‐level rules and classifies given training data sets into positive data sets and negative ones. The rule refinement process tries to refine an initial object‐level rule base on classified data sets by using four refinement strategies. Unifying these two processes, one can obtain a refined object‐level rule base with high performance where a meta‐level rule selects a data set applicable to it. In order to evaluate the framework, an experiment on real Japanese stock price data shows that a refined object‐level rule base, which comes from the initial object‐level rule base for representing Granville's Law, has a performance beyond that of the average stock price. The performance is difficult for human technical analysts in a stock market to achieve. The result implies that the framework could create an anomaly from Granville's Law in a stock market technical analysis.
APX: An Integrated Knowledge‐Based System to Support Audit Planning
Authors: Bharadwaj Anandhi , Karan Vijay , Mahapatra Radha K. , Murthy Uday S. , Vinze Ajay S. 
Published: March 2014

AbstractAuditors who must express an opinion on the financial statements of publicly held enterprises must perform a series of risk assessments as the basis for selecting appropriate audit procedures to be performed. Although many auditing expert systems have been developed for individual components of the audit risk assessment process such as inherent and control risk assessment, none integrate these assessments to arrive at the ultimate objective of assessing the allowable level of detection risk which drives audit procedure selection. This paper discusses critical design concerns for audit planning expert systems in light of the integrated nature of the steps involved in the audit planning process. The design and development of an integrated audit planning expert system called APX (Audit Planning eXpert) is also discussed. A distinguishing feature of APX is that it not only performs the individual types of risk assessments but it also integrates the resulting assessments to arrive at allowable detection risk. The knowledge acquisition and system development process are described to illustrate some of the mechanics of developing such a system.
Using Rule Induction for Expert System Development: The Case of Asset Writedowns
Authors: Ragothaman Srinivasan , Naik Bijayananda 
Published: March 2014

AbstractThere was a significant increase in the frequency and magnitude of asset writedowns by US firms during the 1980s. Auditors, financial analysts and regulators have shown considerable interest in evaluating the writedown phenomenon. This paper reports on the use of inductive learning to discover knowledge in financial data structures and describes the development and testing of a prototype expert system, WDXPERT, which evaluates asset writedowns. Real‐world data relating to writedown and non‐writedown firms are used for rule induction. A set of training examples comprising 42 writedown and 25 non‐writedown firms are used to generate the rules using IXL, a machine‐learning program. A separate holdout sample containing 43 writedown and 25 non‐writedown firms are used to validate the expert system that incorporates these rules. A second validation procedure is performed by comparing the performance of the expert system with a conventional discriminant analysis model and a logit model using the same data sets. The results indicate that the expert system, WDXPERT, is a useful classification tool to group firms into writedown and non‐writedown classes.
A Comparative Analysis of Artificial Neural Networks Using Financial Distress Prediction
Authors: Fanning Kurt M. , Cogger Kenneth O. 
Published: March 2014

AbstractThis paper examines the efficiency of a generalized adaptive neural network algorithm (GANNA) processor in comparison to earlier model‐based methods, a back‐propagation artificial neural network, and logistic regression approaches to data classification. The research uses the binary classification problem of discriminating between failing and non‐failing firms to compare the methods. The results indicate the potential in time savings and the successful classification results available from a GANNA processor.
A Little AI Goes a Long Way on Wall Street
Authors: Leinweber David , Beinart Yossi 
Published: March 2014

AbstractThis is a history and technical overview of one of the major AI successes in securities trading. It incorporates only as much AI as the financial user community could deal with, integrating tightly with their electronic environment. Computational resources are used to make a simple, highly application‐specific user interface, a small firm has succeeded in developing a system used by many of the largest institutional investors and money managers in the USA. The system is directly linked to the NYSE and other electronic equity‐execution systems. This is not a prototype or a proposal. This is real and in wide use today, often generating transaction volumes exceeding 5 million shares per day. Traders use a special‐purpose rule‐based language to describe a wide variety of market conditions. The system keeps up with high‐speed incoming market data in real‐time and tells the traders when, and how strongly, their specified conditions match the current state of the market. It can also formulate trading recommendations based on those conditions. Finally, and most importantly, it provides direct access to electronic execution channels for quick action on these recommendations. The combination of tight integration with both market data and electronic executions, an appropriate, accessible user interface, and a high level of support have contributed to a major AI success story. The transactions flowing through these systems produce more revenue on a good day than many other AI applications will generate over their entire operational lifetimes.
Analysis of the Effectiveness of Machine Learning in Determining Decision Rules for Executive Compensation Planning
Authors: Michaelsen Robert H. , Swigger Kathleen M. 
Published: March 2014

AbstractThis paper describes the use of a machine‐learning technique to derive executive compensation planning rules. The specific learning technique was AQ15, which constructs rules by classifying sample data according to features or characteristics. The validity of the resulting rules was tested by obtaining a measure of performance called an estimate of the error rate. The machine‐derived rules were then compared to rules made by human experts and by two other versions of AQ15. Although not statistically significant, the machine‐learning version of AQ15 using hypotheses from human experts outperformed the other methods of rule construction.
A Neural Network Analysis of Mortgage Choice
Authors: Grudnitski G. , Do A. Quang , Shilling J. D. 
Published: March 2014

AbstractThere exists today an unanswered question as to whether, and the degree to which, borrower characteristics impact the choice between fixed and adjustable rate mortgages. In this paper, we apply a neural network analysis to supply evidence that answers this question. We find evidence that the characteristics of a borrower's net worth, marital status and education level and whether a co‐borrower is involved contribute in a significant way to the neural network's ability to determine mortgage choice. Further, we show how, because of the facility of neural networks in modeling intrasample differences, they achieve material and statistically significant accuracy gains over qualitative choice models in predicting whether a borrower will choose a fixed or adjustable rate mortgage.
AMTexpert: An Expert Decision Support System for the Corporate Alternative Minimum Tax
Authors: Craig Caroline Kern , Silhan Peter A. 
Published: March 2014

AbstractAMTexpert is a prototype of an expert decision support system (EDSS) for the corporate alternative minimum tax (AMD. The corporate AMT was introduced as part of the Tax Reform Act of 1986 in order to force US corporations to pay more tax. The complicated and interrelated nature of the AMT system has made compliance and planning even more difficult for corporate taxpayers. Given the complexities of the corporate AMT, significant benefits may accrue from the development of an EDSS. In effect, an EDSS combines the judgmental benefits of an expert system (ES) with the computational benefits of a decision support system (DSS). AMTexpert is menu driven with six interdependent phases. The heart of the system is a ‘scoreboard’ used to compare and evaluate alternative tax positions. Context‐sensitive advice and explanations are provided to guide the user. The system was validated and evaluated by two groups of outside experts. AMTexpert demonstrates that an EDSS may be developed from existing tax and systems software.
An Analysis of Potential Legal Liability Incurred Through Audit Expert Systems
Authors: Sutton Steve G. , Young Ronald , McKenzie Phyllis 
Published: March 2014

AbstractThe introduction of expert systems technology into the audit environment has opened a new avenue of auditor legal liability. This paper examines the potential impact expert systems will have on auditor liability. The presentation of this new avenue of auditors' legal liability explores both the potential for litigation under failure of auditor/expert system collaboration to yield prudent decisions and the failure to use an available expert system. The risks evolving from failure to use an available expert system include the possibility that the system could be used against the auditor in the courtroom. While case law will ultimately determine the bounds of this liability, this paper acquaints the reader with the important legal issues involved and the varied outcomes that could emerge. It should also be noted that while the specific example presented in this paper relates to the audit profession, the legal concepts are of equivalent concern to other professions enduring broad implementation of expert systems.
Cooperative Knowledge Processing: The Key Technology for Future Organizations
Authors: Kirn Stefan 
Published: March 2014

AbstractDrawing from the challenges organizations are faced with today, there is a growing understanding that future market success and long‐term survival of enterprises will increasingly be related to the effectiveness of information technology utilization. This, however, requires us to intertwine much more seriously organizational theory and research in information processing than previously. In this paper we approached this aim from the perspective of radically decentralized, computerized enterprises. We further assume that organizations are increasingly process‐oriented, rather than applying to structuring organizations based on task decomposition and assignment. This scenario reveals that, due to the inherent autonomy of organizational units, the coordination of decentralized organizational activities (workflows, processes) necessitates a cooperative style of problem solving. On this basis, the paper introduces the research area of cooperative knowledge processing, with a particular focus on multi‐agent decision support systems and human‐computer cooperative work. Finally, several important organizational applications of cooperative knowledge processing are presented that demonstrate how future enterprises can take great advantage from these new technologies.
Evaluating Competitive Strategies
Authors: Moss Scott , Dixon Huw David , Wallis Steven 
Published: March 2014

AbstractIn this paper we introduce a conception of learning which is a natural extension of economists' representations of learning and which is natural to develop using KBS technology. In the particular form of KBS we use, rule conditions and actions are well‐formulated formulae of first‐order predicate logic (FOPL). As a result, the simulation results obtained from these models are no less analytical than those of pure analytic models. Our results are further strengthened by an experimental design for simulations of competitive behaviour which eliminates implicit bias in the selection of possible behaviours. The system is applied to the Cournot duopoly model. We find that modest intelligence on the part of at least one duopolist systematically increases the profits of both.
New Technologies for Customer Rating: Integration of Knowledge‐Based Systems and Human Judgement
Authors: Wolf Michael F. 
Published: March 2014

AbstractAvoiding incorrect credit decisions and minimizing the required effort are crucial factors for the economic success of a bank. A basic task of credit granting is a correct valuation of a customer's annual account. CUBUS was designed to give general valuation and a first global impression of a company quickly. It also draws the user's attention to the most critical items of the balance sheet. In addition, detailed analysis and valuations of each annual account item is provided. CUBUS goals which have been achieved include increasing decision quality, improving the know‐how level of the staff, improving communication among different departments and finally defining and establishing generally accepted guidelines and norms. The system allows very flexible human‐computer cooperation. It works as a competent assistant which provides the following functions: autonomous valuations, reactive assistance, active consulting, interactive problem solving, tutoring and supervising. When SBC's (Swiss Bank Corporation) general management decided that all commercial customers had to be rated, an application had to be developed to make CUBUS judgements available to all credit specialists within the existing mainframe database environment. The integration of mainframe databases and transactions, knowledge‐based systems running on workstations, and the judgement of human users raised the challenging question of what general principles have to be applied when such diverse components have to be combined in the most efficient way.
Electrodermal responses and memory recall in migraineurs and headache‐free controls
Authors: Tomé‐Pires C. , Miró J. 
Published: March 2014

AbstractBackgroundChronic pain patients show increased peripheral activity when exposed to stimuli relevant to their pain problem. It has been suggested that in migraine there is a highly specific conditioning or sensitization to pain stimuli. The aims of this study were to analyse whether migraineurs (1) are sensitive not only to pain‐related but also to other negative emotional words; and (2) show a memory bias for pain words, when compared to headache‐free controls.MethodsSixty‐six adults participated in the study ( years; SD = 7). They observed 30 words (pain or negative emotional or neutral) in a pseudo‐randomized order. Subsequently, participants were asked to recall the words presented during the trial.ResultsSkin conductance responses (SCRs) induced by pain descriptors and emotional words were very similar to each other and significantly larger than those induced by neutral words; however, there were no differences between both groups in SCRs. Significant differences in immediate memory recall were found between the two groups: migraineurs recalled more emotional words than controls.ConclusionsThe findings suggest that in migraine, not only pain‐related but also negative emotional words may act as triggers. These outcomes may have therapeutic implications as interventions could target problematic pain‐related memories that influence migraine pain perception and pain‐related physiological responses.
Properties of dehydrated human amnion/chorion composite grafts: Implications for wound repair and soft tissue regeneration
Authors: Koob Thomas J. , Lim Jeremy J. , Massee Michelle , Zabek Nicole , Denozière Guilhem 
Published: March 2014

AbstractPURION® processed dehydrated human amnion/chorion membrane (dHACM; MiMedx Group, Marietta, GA) tissue products were analyzed for the effectiveness of the PURION® process in retaining the native composition of the amniotic membrane and preserving bioactivity in the resulting products. dHACM was analyzed for extracellular matrix (ECM) composition through histological staining and for growth factor content via multiplex ELISA arrays. Bioactivity was assessed by evaluating endogenous growth factor production by human dermal fibroblasts in response to dHACM and for thermal stability by mechanical tests and in vitro cell proliferation assays. Histology of dHACM demonstrated preservation of the native amnion and chorion layers with intact, nonviable cells, collagen, proteoglycan, and elastic fibers distributed in the individual layers. An array of 36 cytokines known to regulate processes involved in inflammation and wound healing were identified in dHACM. When treated with dHACM extracts, bioactivity was demonstrated through an upregulation of basic fibroblast growth factor, granulocyte colony‐stimulating factor, and placental growth factor biosynthesis, three growth factors involved in wound healing, by dermal fibroblasts in vitro. After conditioning at temperatures ranging from −78.7 to +73.5°C, dHACM retained its tensile strength and ability to promote proliferation of dermal fibroblasts in vitro. Elution experiments demonstrated a soluble fraction of growth factors that eluted from the tissue and another fraction sequestered within the matrix. The PURION® process retains the native composition of ECM and signaling molecules and preserves bioactivity. The array of cytokines preserved in dHACM are in part responsible for its therapeutic efficacy in treating chronic wounds by orchestrating a “symphony of signals” to promote healing. © 2014 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 102B: 1353–1362, 2014.