Morgan C. Wang

Efficacy of relapse prevention: A meta-analytic review

Although relapse prevention (RP) has become a widely adopted cognitive-behavioral treatment intervention for alcohol, smoking, and other substance use, outcome studies have yielded an inconsistent picture of the efficacy of this approach or conditions for maximal effectiveness. A meta-analysis was performed to evaluate the overall effectiveness of RP and the extent to which certain variables may relate to treatment outcome. Twenty-six published and unpublished studies with 70 hypothesis tests representing a sample of 9,504 participants were included in the analysis. Results indicated that RP was generally effective, particularly for alcohol problems. Additionally, outcome was moderated by several variables. Specifically, RP was most effective when applied to alcohol or polysubstance use disorders, combined with the adjunctive use of medication, and when evaluated immediately following treatment using uncontrolled pre-post tests.

Travel-Time Prediction for Freeway Corridors

The application of a nonlinear time series model to the prediction of traffic parameters on a freeway network is investigated. The nonlinear time series approach is a statistical technique that has strong potential for on-line implementation. A new approach for predicting corridor travel times is developed and tested with travel-time data. The travel-time data are derived from observed speed data, which are collected from an 18-km (11.2-mi) freeway section in Orlando, Florida. The westbound Interstate-4 morning peak period (6:00 to 10:00 a.m.) for 20 incident-free days is tested with the goal of predicting recurrent congestion. The problem is addressed from the perspectives of single-variable and multiple-variable prediction of corridor travel times. In single-variable prediction, speed time-series data are used to forecast travel times along the freeway corridor. A calibrated single-variable prediction model is developed through the application of decay factors to smooth out the input data and the establishment of a threshold on the minimum speed prediction permitted. Multivariable prediction schemes are developed using speed, occupancy, and volume data provided by inductive loop detectors on the study section. The prediction performance of the calibrated single-variable model is shown to be superior to the multivariable prediction schemes. This new approach produces reasonable errors for short-term (5-min) travel-time predictions. The developed model can be implemented on-line with minimal effort.

A survey of data mining techniques for malware detection using file features

This paper presents a survey of data mining techniques for malware detection using file features. The techniques are categorized based upon a three tier hierarchy that includes file features, analysis type and detection type. File features are the features extracted from binary programs, analysis type is either static or dynamic, and the detection type is borrowed from intrusion detection as either misuse or anomaly detection. It provides the reader with the major advancement in the malware research using data mining on file features and categorizes the surveyed work based upon the above stated hierarchy. This served as the major contribution of this paper.

Is the Curve Relating Temperature to Aggression Linear or Curvilinear? Assaults and Temperature in Minneapolis Reexamined

Using archival data from Minneapolis recorded in 3-hr time intervals, E. G. Cohn and J. Rotton concluded that there is an inverted U-shaped relationship between temperature and assault, with the maximum assault rate occurring at 74.9 degrees F. They depicted this relationship by plotting temperature against assault. This plot, however, fails to take into account time of day. Time of day was strongly related to both temperature and assault, but in opposite directions. Between 9:00 p.m. and 2:59 a.m. of the next day, when most assaults occurred, there was a positive linear relationship between temperature and assault. The Minneapolis data actually provide stronger support of a positive linear (or monotonic) relationship between temperature and assault than of an inverted U-shaped relationship.

Maximum Likelihood Regression Trees

We propose a method of constructing regression trees within the framework of maximum likelihood. It inherits the backward fitting idea of classification and regression trees (CART) but has more rigorous justification. Simulation studies show that it provides more accurate tree model selection compared to CART. The analysis of a baseball dataset is given as an illustration.

A procedure for combining sample standardized mean differences and vote counts to estimate the population standardized mean difference in fixed event models.

Missing effect-size estimates pose a difficult problem in meta-analysis. Conventional procedures for dealing with this problem include discarding studies with missing estimates and imputing single values for missing estimates (e.g., 0, mean). An alternative procedure, which combines effect-size estimates and vote counts, is proposed for handling missing estimates. The combined estimator has several desirable features: (a) It uses all the information available from studies in a research synthesis, (b) it is consistent, (c) it is more efficient than other estimators, (d) it has known variance, and (e) it gives weight to all studies proportional to the Fisher information they provide. The combined procedure is the method of choice in a research synthesis when some studies do not provide enough information to compute effect-size estimates but do provide information about the direction or statistical significance of results.

A numerical method for accurately approximating multivariate normal probabilities

Abstract A Taylor series expansion of the multivariate normal integral is used to calculate the value of the integral over rectangular regions. Interval analysis and automatic differentiation provide self-validation for calculated probabilities. In examples, the Taylor series approximation gives more accurate results than the algorithm of Schervish (1984).

Comparison of algorithms for bivariate normal probability over a rectangle based on self-validated results from interval analysis

Comparison of algorithms for computing probabilities and percentiles is often carried out in an effort to identify the best algorithm for various applications. One requirement when conducting comparative studies is some useable source of “satisfactory approximations to correct answers” to use as a basis when making accuracy comparisons. This paper reports success in applying elements of interval analysis to obtain a self-validating computational method for Bivariate Normal Probabilities. Results from applying this method can be used to provide a basis for accuracy studies of algorithms for Bivariate Normal probabilities. A study to compare several methods for computing probabilities over rectangles for this probability distribution, using the self-validated bases values, was carried out. The paper reports a choice of best method.

Detecting Trojans Using Data Mining Techniques

A trojan horse is a program that surreptitiously performs its operation under the guise of a legitimate program. Traditional approaches using signatures to detect these programs pose little danger to new and unseen samples whose signatures are not available. The focus of malware research is shifting from using signature patterns to identifying the malicious behavior displayed by these malwares. This paper presents the novel idea of extracting variable length instruction sequences that can identify trojans from clean programs using data mining techniques. The analysis is facilitated by the program control flow information contained in the instruction sequences. Based on general statistics gathered from these instruction sequences, we formulated the problem as a binary classification problem and built random forest, bagging and support vector machine classifiers. Our approach showed a 94.0% detection rate on novel trojans whose data was not used in the model building process.

Identifying At-Risk Students for Early Interventions—A Time-Series Clustering Approach

The purpose of this paper is to identify at-risk online students earlier, more often, and with greater accuracy using time-series clustering. The case study showed that the proposed approach could generate models with higher accuracy and feasibility than the traditional frequency aggregation approaches. The best performing model can start to capture at-risk students from week 10. In addition, the four phases in student’s learning process detected holiday effect and illustrate at-risk students’ behaviors before and after a long holiday break. The findings also enable online instructors to develop corresponding instructional interventions via course design or student–teacher communications.