Pattern matching-based prediction using affine combination of two measures: two are better than one

Gait stability has been measured by using many entropy-based methods. However, the relation between the entropy values and gait stability is worth further investigation. A research reported that average entropy (AE), a measure of disorder, could measure the static standing postural stability better than multiscale entropy and entropy of entropy (EoE), two measures of complexity. This study tested the validity of AE in gait stability measurement from the viewpoint of the disorder. For comparison, another five disorders, the EoE, and two traditional metrics methods were, respectively, used to measure the degrees of disorder and complexity of 10 step interval (SPI) and 79 stride interval (SI) time series, individually. As a result, every one of the 10 participants exhibited a relatively high AE value of the SPI when walking with eyes closed and a relatively low AE value when walking with eyes open. Most of the AE values of the SI of the 53 diseased subjects were greater than those of the 26 healthy subjects. A maximal overall accuracy of AE in differentiating the healthy from the diseased was 91.1%. Similar features also exists on those 5 disorder measurements but do not exist on the EoE values. Nevertheless, the EoE versus AE plot of the SI also exhibits an inverted U relation, consistent with the hypothesis for physiologic signals.

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Two Are Better Than One: Adaptive Sparse System Identification Using Affine Combination of Two Sparse Adaptive Filters

2014 IEEE 79th Vehicular Technology Conference (VTC Spring) ◽

10.1109/vtcspring.2014.7023132 ◽

2014 ◽

Cited By ~ 5

Author(s):

Guan Gui ◽

Shinya Kumagai ◽

Abolfazl Mehbodniya ◽

Fumiyuki Adachi

Keyword(s):

System Identification ◽

Adaptive Filters ◽

Sparse System ◽

Sparse System Identification ◽

Affine Combination ◽

Better Than

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The Effect of Audit Committee Industry Expertise on Monitoring the Financial Reporting Process

The Accounting Review ◽

10.2308/accr-50585 ◽

2013 ◽

Vol 89 (1) ◽

pp. 243-273 ◽

Cited By ~ 115

Author(s):

Jeffrey R. Cohen ◽

Udi Hoitash ◽

Ganesh Krishnamoorthy ◽

Arnold M. Wright

Keyword(s):

Financial Reporting ◽

Audit Committee ◽

Data Availability ◽

Holistic View ◽

Reporting Process ◽

Two Measures ◽

External Auditor ◽

Accounting Expertise ◽

Better Than ◽

Industry Expertise

ABSTRACT Calls from practice suggest that audit committee members with industry expertise can improve audit committee effectiveness. Nevertheless, regulators and the extant literature have focused on the financial expertise of the audit committee. We posit that audit committee industry knowledge is valuable because accounting guidance, estimates, and oversight of the external auditor are often linked to a company's operations within a particular industry. Taking a holistic view, we examine two measures of financial reporting quality (financial restatements and discretionary accruals) and two measures of external auditor oversight (audit and nonaudit fees). As predicted, we find that audit committee members who are both accounting and industry experts perform better than those with only accounting expertise. We also find that in certain instances, supervisory experts who are also industry experts perform better than supervisory experts alone. Overall, these results suggest that industry expertise, when combined with accounting expertise, can improve the effectiveness of the audit committee in monitoring the financial reporting process. Data Availability: All data are gathered from publicly available sources.

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A Fast Engine for Multi-String Pattern Matching

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001417500392 ◽

2017 ◽

Vol 31 (12) ◽

pp. 1750039 ◽

Cited By ~ 2

Author(s):

Zhan Peng ◽

Yuping Wang ◽

Wei Yue

Keyword(s):

Data Structure ◽

Pattern Matching ◽

Large Scale ◽

String Matching ◽

Memory Consumption ◽

Matching Problems ◽

Text String ◽

Tree Node ◽

String Pattern ◽

Better Than

Multi-string matching (MSM) is a core technique searching a text string for all occurrences of some string patterns. It is widely used in many applications. However, as the number of string patterns increases, most of the existing algorithms suffer from two issues: the long matching time, and the high memory consumption. To address these issues, in this paper, a fast matching engine is proposed for large-scale string matching problems. Our engine includes a filter module and a verification module. The filter module is based on several bitmaps which are responsible for quickly filtering out the invalid positions in the text, while for each potential matched position, the verification module confirms true pattern occurrence. In particular, we design a compact data structure called Adaptive Matching Tree (AMT) for the verification module, in which each tree node only saves some pattern fragments of the whole pattern set and the inner structure of each tree node is chosen adaptively according to the features of the corresponding pattern fragments. This makes the engine time and space efficient. The experiments indicate that, our matching engine performs better than the compared algorithms, especially for large pattern sets.

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Preservice Teachers’ Mathematics Teaching Competence: Comparing Performance on Two Measures

Journal of Teacher Education ◽

10.1177/0022487117753575 ◽

2018 ◽

Vol 70 (5) ◽

pp. 472-484 ◽

Cited By ~ 2

Author(s):

Rossella Santagata ◽

Judith Haymore Sandholtz

Keyword(s):

Preservice Teachers ◽

Performance Assessment ◽

Mathematics Teaching ◽

Future Research ◽

Single Measure ◽

Practicing Teachers ◽

Teaching Competence ◽

Two Measures ◽

The Relationship ◽

Better Than

This exploratory study examines the relationship between preservice teachers’ performance on a teaching performance assessment for licensure in elementary mathematics and a measure of knowledge that, in studies of practicing teachers, was found to predict effective mathematics teaching. A sample of 89 preservice teachers completed the Performance Assessment for California Teachers (PACT) and the classroom video analysis (CVA) instrument. Correlation analyses found overall weak associations between measures for the whole group. For groups of high, medium, and low performers on PACT, average scores on the CVA measure and its subscales varied in the predicted direction. However, individuals within the PACT performing groups had a relatively broad range of scores on the CVA, and several preservice teachers who performed poorly on PACT had average or better than average performance on the CVA. Similarly, several of the PACT high performers scored below the whole group CVA average. In addition to suggesting areas for future research, the findings raise questions about assessing preservice teachers’ readiness to teach mathematics and the use of a single measure to make licensing decisions.

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Preliminary Test Estimators and Confidence Intervals for the Parametric Functions of the Moore and Bilikam Family of Lifetime Distributions Based on Records

Austrian Journal of Statistics ◽

10.17713/ajs.v48i4.844 ◽

2019 ◽

Vol 48 (4) ◽

pp. 58-89

Author(s):

Ajit Chaturvedi ◽

Suk-Bok Kang ◽

Ananya Malhotra

Keyword(s):

Confidence Intervals ◽

Empirical Bayes ◽

Preliminary Test ◽

Minimum Variance ◽

Lifetime Distributions ◽

Expected Length ◽

Two Measures ◽

Parametric Functions ◽

Empirical Bayes Estimator ◽

Better Than

We consider two measures of reliability functions namely R(t)=P(X>t) and P=P(X>Y) for the Moore and Bilikam (1978) family of lifetime distributions which covers fourteen distributions as specific cases. For record data from this family of distributions, preliminary test estimators (PTEs) and preliminary test confidence interval (PTCI) based on uniformly minimum variance unbiased estimator (UMVUE), maximum likelihood estimator (MLE), empirical Bayes estimator (EBE) are obtained for the parameter. The bias and mean square error (MSE) (exact and asymptotic) of the proposed estimators are derived to study their relative efficiency and through simulation studies we establish that PTEs perform better than ordinary UMVUE, MLE and EBE. We also obtain the coverage probability (CP) and the expected length of the PTCI of the parameter and establish that the confidence intervals based on MLE are more precise. An application of the ordinary preliminary test estimator is also considered. To the best of the knowledge of the authors, no PTEs have been derived for R(t) and P based on records and thus we define improved PTEs based on MLE and UMVUE of R(t) and P. A comparative study of different methods of estimation done through simulations establishes that PTEs perform better than ordinary UMVUE and MLE.

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A NOVEL ALGORITHM FOR SOLVING THE STRING MATCHING PROBLEM

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026806002040 ◽

2006 ◽

Vol 06 (04) ◽

pp. 499-510 ◽

Cited By ~ 1

Author(s):

IBRAHIEM M. M. EL EMARY ◽

MOHAMMED S. M. JABER

Keyword(s):

Pattern Matching ◽

Word Length ◽

String Matching ◽

Specific Pattern ◽

Experimental Results ◽

Search Process ◽

Matching Problem ◽

Pattern Length ◽

Novel Algorithm ◽

Better Than

The string matching problem consists of finding one or more, generally all, exact occurrences of a pattern P in a text T. This paper presents a new algorithm for solving the string matching problem. Application of the proposed algorithm assists in improving the search process of a specific pattern in a certain unchangeable text through decreasing the number of character comparisons. Operation concept of such an algorithm depends on pattern reading to obtain the pattern length and the pattern first character and then a search is done in a table of two columns: the first column represents the word length in the text and the second one represents the start positions of each word classified by the same length. After that the algorithm just searches the words of the same length. Our experimental results depend mainly on comparing the performance of our algorithm with the well-known pattern matching algorithms such as Boyer–Moor's and Boyer–Moor–Galil's. The comparison between our algorithm and others are done in terms of the number of characters compared for different sizes of text. The output results show that our algorithm performs better than the others in terms of this parameter.

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Pengaruh PW, KWL, and DRTA Terhadap Hasil Belajar Rekognisi dan Recall Matapelajaran Sejarah Indonesia di SMA

Metafora: Education, Social Sciences and Humanities Journal ◽

10.26740/metafora.v3n1.p53-72 ◽

2019 ◽

Vol 3 (1) ◽

pp. 53

Author(s):

Susanto Yunus Alfian

Keyword(s):

Group Comparison ◽

Post Hoc Analysis ◽

Tenth Graders ◽

Historical Texts ◽

Activity Group ◽

Two Measures ◽

Directed Reading ◽

Post Hoc ◽

Static Group ◽

Better Than

This research explores the effects of three instructional methods (Picture Walk, Know-Want To Learn, and Directed Reading Thinking Activity) on the student’s recognition and recall of historical texts. The subjects were 247 tenth graders of SMA Negeri 1 Sumberpucung. Static-group comparison design was employed that examined 3 treatments: picture walk group, know-want to learn group, and Directed Reading Thinking Activity group. To evaluate the treatment effects, MANOVA was conducted on two measures (recognition and recall tests). Results indicated that PW, KWL, and DRTA yield statistically significant effects on recognition and recall tests. Using post hoc analysis, DRTA is better than the two other methods.

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Measures Of Investor Sentiment: A Comparative Analysis Put-Call Ratio Vs. Volatility Index

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v6i8.2458 ◽

2011 ◽

Vol 6 (8) ◽

Cited By ~ 7

Author(s):

Arindam Bandopadhyaya ◽

Anne Leah Jones

Keyword(s):

Asset Prices ◽

Investor Sentiment ◽

Economic Factors ◽

Short Term ◽

Market Sentiment ◽

Volatility Index ◽

Daily Data ◽

Traditional Research ◽

Two Measures ◽

Better Than

Traditional research on asset pricing has focused on firm-specific and economy-wide factors that affect asset prices.  Recently, the finance literature has turned to non-economic factors, such as investor sentiment, as possible determinants of asset prices (see for example, Fisher and Statman 2000 and Baker and Wurgler 2006).  Studies such as Baek, Bandopadhyaya and Du (2005) suggest that shifts in investor sentiment may explain short-term movements in asset prices better than any other set of fundamental factors.  A wide array of investor sentiment measures are now available, which leads us quite naturally to the question of which measure best mirrors actual market movements.   In this paper, we begin to address this question by comparing two measures of investor sentiment which are computed daily by the Chicago Board Options Exchange (CBOE) and for which historical data are freely available on the CBOE website, thus making them ideal for use by both academics and practitioners studying market behavior: the Put-Call Ratio (PCR) and the Volatility Index (VIX).  Using daily data from January 2, 2004 until April 11, 2006, we find that the PCR is a better explanatory variable than is the VIX for variations in the S&P 500 index that are not explained by economic factors.  This supports the argument that, if one were to choose between these two measures of market sentiment, the PCR is a better choice than the VIX.

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