bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design of a medical‐grade QoS metric for wireless environments
Authors: Park Kyung‐Joon , Lee Hyung‐Ho , Choi Sunghyun , Kang Kyungtae 
Published: March 2014

AbstractIn this letter, we introduce medical‐grade quality of service (QoS) for wireless healthcare applications. We first investigate the basic QoS requirements of medical applications. We point out that the conventional QoS metric of the packet error rate (PER) is insufficient for evaluating the QoS level of medical applications. The most critical concern in medical‐grade QoS is whether the received data can be diagnosed by medical personnel, that is, medical diagnosability. As a prevailing application, we present a medical‐grade QoS metric for wireless electrocardiogram (ECG) transmission. The introduced QoS metric, called weighted diagnostic distortion (WDD), can properly evaluate medical‐grade QoS by reflecting the main diagnostic features of an ECG signal. Our simulation results demonstrate that there can be a significant discrepancy between WDD and PER, which confirms the importance of developing a medical‐grade QoS metric by properly taking into account the key characteristics of medical traffic. Copyright © 2014 John Wiley & Sons, Ltd.
Power‐aware routing in networks with quality of services constraints
Authors: Lin Gongqi , Soh Sieteng , Chin Kwan‐Wu , Lazarescu Mihai 
Published: March 2014

AbstractCurrent network infrastructures are over‐provisioned and thus exhibit poor power efficiency at low traffic load. We consider networks consisting of bundled links, whereby each link has one or more physical cables that can be switched off independently. The problem at hand is to switch off redundant nodes and cables during off‐peak periods, while retaining the quality of services provided to existing traffic demands. Unfortunately, the problem to maximally shutdown redundant nodes and cables is Non‐deterministic Polynomial‐time (NP)‐complete. Henceforth, we design a fast heuristic, called Multiple Paths by Shortest Path First (MSPF), that aims to maximise the number of switched‐off nodes and cables subject to satisfying maximum link utilisation (MLU) and path length (PL) constraints. We have extensively evaluated the performance of MSPF on both real and synthetic topologies and traffic demands. Further, we have compared its performance against two state‐of‐the‐art techniques: GreenTE, usable when each link has one cable, and Fast Greedy Heuristic (FGH), which supports bundled links but only for networks without MLU and PL constraints. On the Sprint network, MSPF can save on average 5% more power as compared to GreenTE while incurring only 1% of GreenTE's running time. While yielding equivalent power savings, MSPF requires only 0.35% of FGH's running time. Finally, setting MLU to at most 50% and PL to no longer than the network diameter, MSPF reduces the power usage of the GÉANT topology by up to 91% for links consisting of 10 cables. For experiments on synthetic topologies with bundle size of 30, MSPF yields a power saving of 89.81%. Copyright © 2014 John Wiley & Sons, Ltd.
Disrupted network architecture of the resting brain in attention‐deficit/hyperactivity disorder
Authors: Sripada Chandra , Kessler Daniel , Fang Yu , Welsh Robert C. , Prem Kumar Krishan , Angstadt Michael 
Published: March 2014

AbstractBackgroundAttention‐deficit/hyperactivity disorder (ADHD) is one of the most prevalent psychiatric disorders of childhood. Neuroimaging investigations of ADHD have traditionally sought to detect localized abnormalities in discrete brain regions. Recent years, however, have seen the emergence of complementary lines of investigation into distributed connectivity disturbances in ADHD. Current models emphasize abnormal relationships between default network—involved in internally directed mentation and lapses of attention—and task positive networks, especially ventral attention network. However, studies that comprehensively investigate interrelationships between large‐scale networks in ADHD remain relatively rare.MethodsResting state functional magnetic resonance imaging scans were obtained from 757 participants at seven sites in the ADHD‐200 multisite sample. Functional connectomes were generated for each subject, and interrelationships between seven large‐scale brain networks were examined with network contingency analysis.ResultsADHD brains exhibited altered resting state connectivity between default network and ventral attention network [P < 0.0001, false discovery rate (FDR)‐corrected], including prominent increased connectivity (more specifically, diminished anticorrelation) between posterior cingulate cortex in default network and right anterior insula and supplementary motor area in ventral attention network. There was distributed hypoconnectivity within default network (P = 0.009, FDR‐corrected), and this network also exhibited significant alterations in its interconnections with several other large‐scale networks. Additionally, there was pronounced right lateralization of aberrant default network connections.ConclusionsConsistent with existing theoretical models, these results provide evidence that default network‐ventral attention network interconnections are a key locus of dysfunction in ADHD. Moreover, these findings contribute to growing evidence that distributed dysconnectivity within and between large‐scale networks is present in ADHD. Hum Brain Mapp 35:4693–4705, 2014. © 2014 Wiley Periodicals, Inc.
Knowledge Representation Issues in Complex Decision Domains: an Example from Inherent Audit Risk Assessment
Authors: Peters James M. 
Published: March 2014

AbstractThere has been significant discussion in artificial intelligence and expert systems concerning different representation systems for complex domain knowledge. This article discusses the strengths, weaknesses and psychological validity of two common systems, associational or rule‐ and model‐based, and presents a computer model which incorporates both approaches into a hybrid system. The computer model reasons in the complex decision domain of inherent audit risk assessment.
Case‐based Reasoning and Multiple‐agent Systems for Accounting Regulation Systems with Extensions
Authors: O'Leary Daniel E. 
Published: March 2014

AbstractOne of the areas of judgment research in accounting and financial applications is that of accounting regulation. Previously, artificial intelligence efforts at modeling human judgment in accounting regulation systems have concentrated on rule‐based expert systems. In those systems, general heuristic knowledge was captured using ‘if … then …’ rules in order to model particular decision processes. Recent developments in artificial intelligence have focused on case‐based reasoning (CBR) and multiple‐agent intelligent systems (MAIS). The ideas behind CBR are that ‘if it worked once then remember to use it again’ and ‘if it did not work before, then remember to not use it again‘. MAIS assumes that many organizational systems can be treated as computational models of multiple‐interacting intelligent agents. Typically, solutions may be derived using some form of negotiations between agents to accomplish single global or separate individual interacting goals. This paper argues that many accounting regulation judgment processes can be modeled using CBR and MAIS. As a result, it summarizes some examples of both CBR and MAIS useful in accounting regulation and extends those to other accounting applications. In addition, it describes the results of some previously developed systems that employ CBR or MAIS.
Case‐Based Reasoning and Risk Assessment in Audit Judgment
Authors: Denna Eric L. , Hansen James V. , Meservy Rayman D. , Wood Larry E. 
Published: March 2014

AbstractThe purpose of this paper is to describe the results of an effort to utilize case‐based reasoning (CBR) to model a specific audit judgment task. To date, most efforts to develop computational models of audit judgment have used strictly rule‐based representation methods. Some researchers have recently adopted more robust structures to model the auditor domain knowledge. Although these recent efforts to extend the representation methods appear to be more accurate descriptions of auditor reasoning and memory, they still lack a comprehensive theory to guide the development of the model. A commonly encountered phenomenon in audit judgment is for an auditor to compare the current case to similar previous experiences. Others have proposed a model for this type of judgment in other expert judgment domains. This model has become known as case‐based reasoning (CBR). This study describes our initial efforts to utilize CBR to model a specific audit judgment task.
A Case Study and Field Evaluation of EDP‐XPERT
Authors: Messier William F. , Hansen James V. 
Published: March 2014

AbstractThis paper reports on the continued evaluation of EDP‐XPERT, and expert system for assisting Computer Audit Specialists (CASs) in evaluating the reliability of EDP controls in advanced computer environments. The current evaluation of the system involved four phases. First, Senior CASs used the system on two case studies. Second, it was then used by the Senior CASs to evaluate recently completed audits. Third, a sensitivity analysis of the system's output was conducted. Fourth, the knowledge base was reprogrammed into another expert system shell and the Senior CASs' responses on the two cases and the audits were re‐run. The results indicated that EDP‐XPERT performed well on the case studies but that performance declined on the audits. The sensitivity analysis showed that EDP‐XPERT's conclusions were affected by moderate perturbations of the CASs' input judgments. Finally, reprogramming the knowledge base into a more flexible expert system shell substantially improved the performance of the system's judgements.
Analysis of Accounting Expert Systems Citations: An Alternative Accounting Classification Scheme
Authors: Brown Carol E. 
Published: March 2014

AbstractThis paper compares and contrasts a paper by Shanker, Nilakanta, and Norris (1992) that quantitatively analyzes citations with Brown (1989). The analytical approach used by Shanker et al. is discussed in light of the background and limitations of the original study, the accounting classification scheme used for the analysis, and classification differences caused by either authors classifying their own articles or someone else classifying the articles based on abstracts. An alternative accounting classification scheme is presented that uses four dimensions to describe and organize the discipline of accounting: accounting tasks, domain, organization type, and service provider. In this context, a number of representative accounting expert systems are classified and discussed. Recommendations are suggested for further research.
On Intelligent Database Systems
Authors: McLeod Dennis , Yanover Paul 
Published: March 2014

AbstractIn response to the limitations of comtemporary database management systems in addressing the requirements of many potential application environments, and in view of the characteristics of emerging interconnected systems, we examine research directions involving adding more ‘intelligence’ to database systems. Three major thrusts in the intelligent database systems area are discussed. The first involves increasing the modeling power to represent an application environment. The second emphasis centers on enhancing the functionality of such a system to include rule‐based inference capabilities. The third direction focuses on supporting the interoperability of multiple, autonomous, heterogeneous database systems. We provide here a view of the first two thrusts, and examine the third via a description of the Remote‐Exchange research effort currently underway at USC.
Artificial Neural Networks Applied to Ratio Analysis in the Analytical Review Process
Authors: Coakley James R. , Brown Carol E. 
Published: March 2014

AbstractExperts claim that artificial neural network (ANN) technology can outperform standard statistical methods when applied to examine actual financial data. Researchers have used ANNs to analyze bankruptcy prediction, bond rating and the going‐concern problem. Financial firms have employed ANNs commercially to predict commercial bank failures, detect credit card fraud and verify signatures. For accounting and auditing problems, however, application of ANN technology has been limited. Preliminary experiments tested whether an ANN offered improved performance in recognizing material misstatements during the analytical review process of auditing. Four years of audited financial data from a medium‐sized distributor were input as data streams to calibrate the ANN across fifteen financial accounts. Researchers compared a presumed lack of actual errors and certain seeded material errors with signals from the ANN analytical review process to evaluate performance. Results were compared to analyses where financial ratios and regression methods were employed as analytical review techniques. Results tentatively suggest that the ANN method recognized patterns within financial accounts more effectively than did financial ratio and regression methods. ANNs applied as a forecasting tool seem useful for identifying patterns that can indicate potential investigations of a firm's unaudited financial data in the current year.
A Research Perspective: Artificial Intelligence, Management and Organizations
Authors: Duchessi Peter , O'Keefe Robert , O'Leary Daniel 
Published: March 2014

AbstractIn recent years many companies have deployed Artificial Intelligence (AI), which has included neural networks, expert systems and voice‐recognition systems. Yet managers and developers understand very little about how management and organizations affect or are affected by the technology. Using specific examples from practice and research, this paper discusses the interaction of AI, management and organizations, and describes some methodological approaches and theoretical models for studying those interactions. It provides direction for future research.
Expert Systems for Evaluating Business Opportunities: Implementing the Management Advisor at Krypton Chemical
Authors: Reitman W. , Shim S.J. 
Published: March 2014

AbstractThis paper investigates issues arising in procuring and implementing a commercial high‐end expert system, Palladian's Management Advisor. This system is intended for improving corporate decisions about financial investments in new business opportunities, e.g. projects involving new plants, equipment, products, or marketing expenditures. We look at Krypton Chemical's experiences with the Management Advisor from both the customer's and the vendor's viewpoints. The discussion raises some difficult questions about what it takes to make high‐end financial expert systems successful. The paper then concludes with recommendations intended to improve design, marketing, procurement and implementation practices with respect to such systems.
The Accuracy of Decision Tree Induction in a Noisy Domain for Expert Systems Construction
Authors: Kim Hyunsoo , Koehler Gary J. 
Published: March 2014

AbstractMany studies have shown that decision tree induction methods could be used to determine rules for expert systems. Pruning techniques are often used to increase the accuracy of an induced decision tree over the instance space. While recent results of decision tree induction show that large samples may be required to induce a decision tree of small error, recent expository studies have used very small sample sizes. In such cases it is of value to obtain a posterior evaluation of the error of the induced concept. In this paper we give three methods to estimate the accuracy of a pruned decision tree. The first method assumes uniform prior distribution. For those cases where uniform prior is not appropriate, we develop a method to obtain appropriate prior using a beta distribution. Finally, we provide a general bound which requires no assumption over the instance space. These results can be used when a pruned decision tree is used to classify the original domain or another close domain.
A Multi‐Stage Model of Decision Bias: Implications for Expert Systems
Authors: Housel Thomas J. , Rodgers Waymond 
Published: March 2014

AbstractThe study of the bias processes that affect decision making is crucial in designing expert systems. This study proposes a multi‐stage model for decision biases which reconceptualizes cognitive styles and decision heuristics within a framework that borrows heavily from research by Posner and McLeod (1982), Tversky and Kahneman (1973) and Ramaprasad (1987). The framework is tested within the context‐bound area of loan making so that the biasing effects of prior experience on decision making can be examined. The results are analyzed with a path‐modeling technique (i.e. covariance structural modeling) that allows testing for indirect as well as direct effects. The results are discussed in terms of the implications for expert systems development
The Fourth International Symposium on Intelligent Systems in Accounting, Finance and Management
Authors: Brown Carol E. , Coakley James R. , Eining Martha M. 
Published: March 2014

AbstractThe Fourth International Symposium on Intelligent Systems in Accounting, Finance and Management ranks as the oldest, on‐going meeting on the use of artificial intelligence (AI) in business. The conference spotlights a wide range of issues with both theoretical/practical and behavioral/technological dimensions.
Non‐Linear Predictive Models for Intra‐Day Foreign Exchange Trading
Authors: Zhang Xiru 
Published: March 2014

AbstractArtificial neural networks were used to search for non‐linear relations in high‐ frequency foreign exchange time series. Three years (1985‐7) tick‐by‐tick bid prices for the Swiss franc to the US dollar exchange rate were used in this study as training data to specify predictive models for intra‐day trading, which was then tested on the same exchange rate time series in the following year (1988). A simple trading rule was adopted to evaluate the models, which showed statistically significant trading profit under moderate transaction costs. In contrast, a standard linear model did not produce profit with the same training and test data and under the same trading rule and transaction cost assumption. This provides evidence for the non‐linear nature of the foreign exchange time series under study.
Using Constraint Programming to Design an Option‐based Decision Support System
Authors: Mizoguchi Fumio , Ohwada Hayato 
Published: March 2014

AbstractFinancial options and futures give investors the opportunity to form complex strategies that meet their investment objectives for risk management. However, this opportunity gives rise to a difficult task: finding a desired strategy from among a large number of possible strategies. This paper describes an intelligent decision‐support system for generating option‐based investment strategies by using constraint programming, which is an integrated framework of Artificial Intelligence and Logic Programming. In this system, constraint programming acts as a bridge between qualitative and quantitative analyses in decision processes. In qualitative analysis, logical reasoning with hypotheses is used to automatically create complex strategies through abstract matching with investors' profiles. Here, abstract matching can be regarded as symbolic computation for producing qualitatively reasonable strategies. In quantitative analysis, a set of complete solutions that satisfy user‐supplied constraints are obtained by constraint satisfaction and optimization. A constraint language based on the framework of Constraint Logic Programming has been developed in order to integrate these symbolic and numerical computations in a uniform way. The resulting system written in this language has the following features. (1) Unlike rule‐based expert systems, the constraint‐based system can create novel investment strategies. (2) A smooth transition from qualitative to quantitative analyses can be naturally achieved due to the constraint language. (3) Qualitative analysis can reduce search complexity, because the analysis focuses on a small set of qualitative distinctions in solution space. These features indicate the usefulness of constraint programming for designing intelligent decision‐support systems.
Application of the Rough Set Approach to Evaluation of Bankruptcy Risk
Authors: Slowinski R. , Zopounidis C. 
Published: March 2014

AbstractWe present a new approach to evaluation of bankruptcy risk of firms based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of information systems representing knowledge gained by experience. The financial information system describes a set of objects (firms) by a set of multi‐valued attributes (financial ratios and qualitative variables), called condition attributes. The firms are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of firms by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the firms analysed and to derive decision rules from the financial information system which can be used to support decisions about financing new firms. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of bankruptcy risk by a Greek industrial development bank is studied using the rough set approach.
Predicting Corporate Failure Using a Neural Network Approach
Authors: Boritz J.E. , Kennedy D.B. , Albuquerque Augusto de Miranda e 
Published: March 2014

AbstractThis paper investigates the performance of Artificial Neural Networks for the classification and subsequent prediction of business entities into failed and non‐failed classes. Two techniques, back‐propagation and Optimal Estimation Theory (OET), are used to train the neural networks to predict bankruptcy filings. The data are drawn from Compustat data tapes representing a cross‐section of industries. The results obtained with the neural networks are compared with other well‐known bankruptcy prediction techniques such as discriminant analysis, probit and logit, as well as against benchmarks provided by directly applying the bankruptcy prediction models developed by Altman (1968) and Ohlson (1980) to our data set. We control the degree of ‘disproportionate sampling’ by creating ‘training’ and ‘testing’ populations with proportions of bankrupt firms ranging from 1% to 50%. For each population, we apply each technique 50 times to determine stable accuracy rates in terms of Type I, Type II and Total Error. We show that the performance of various classification techniques, in terms of their classification errors, depends on the proportions of bankrupt firms in the training and testing data sets, the variables used in the models, and assumptions about the relative costs of Type I and Type II errors. The neural network solutions do not achieve the ‘magical’ results that literature in this field often promises, although there are notable 'pockets' of superior performance by the neural networks, depending on particular combinations of proportions of bankrupt firms in training and testing data sets and assumptions about the relative costs of Type I and Type II errors. However, since we tested only one architecture for the neural network, it will be necessary to investigate potential improvements in neural network performance through systematic changes in neural network architecture.