filmcritic.com presents a review from staff member Robert Strohmeyer. You can find the review with full credits at http://filmcritic.com/misc/emporium.nsf/2a460f93626cd4678625624c007f2b46/7bdd22b907aeefa0882569690006544d?OpenDocument
BEAUTIFUL A film review by Robert Strohmeyer Copyright 2000 filmcritic.com filmcritic.com
Few things simultaneously sicken and seduce the feminist spirit in quite as thorough a way as an American beauty pageant. Now an inextricable fixture on our cultural landscape, the pageant defines, glorifies, and objectifies Woman as a prescription for all time. An evening spent watching the Miss America proceedings on TV is like a psychosexual time warp, dragging us back to a kinder, simpler time when the female of the species need not worry itself over difficult issues like voting or wearing shoes. So it was not without a sense of irony that I walked down the cinema for a screening Sally Field's Beautiful.
Irony is the central force of Field's representative pageantry, and in the tradition of Drop Dead Gorgeous, it is no disappointment. But this is also a human story, about women in search of identity and belonging.
Mona (Minnie Driver) is a dubious beauty queen, having spent her entire life contesting--against all reason and likelihood--for the crown of Miss American Miss.  She has become Miss Illinois. Now, if she can keep her false persona straight, she stands a chance of winning the crown. And if it weren't for the untimely suicide of her best friend's convalescent patient and the disqualifying existence of her 7-year-old daughter (played by Hallie Kate Eisenberg, that annoying little girl from the Pepsi commercials), victory might be simple.
Fortunately for moviegoers, life is not so simple. But equally unfortunately, much of this tale's delivery is bogged down by an incongruous sense of vision.
At the story's outset, it is compelling and funny. Young Mona is precocious and endearing as she struggles to grow up and prove her self worth in the shadow of her alcoholic mother.  But, by the time Driver steps in to play the role, Mona has become little more than a caricature. She is absurd and unloving, one-dimensional in her desire to win pageants and incapable of basic human emotion. This lack of humanity makes the character fundamentally boring to watch, in spite of Driver's obvious talent.
Eisenberg, however, is--though I so hate to admit it--quite good in her role as the young Vanessa (except for the argument scenes, in which her shrill screaming was more deafening than interesting). Once we've made it through the long, irritating period of watching how far Mona's personality has degenerated, Eisenberg's performance as the story's conscience-bearer is a much-needed refreshment.
Sally Field's direction is, for the most part, above par. But this film is disappointingly short on substance. In the end, mothers and daughters may bond over many of the movie's funnier, heartwarming scenes, but most moviegoers will do well to wait for the DVD.

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Tulane University
Bachelor of Science in Engineering
Majors: Biomedical Engineering, Computer Science, Mathematics
GPA: 3.9
Biomedical Engineering
- Statics
- Mechanics of Materials
- Materials Science & Engineering
- Biofluid Mechanics
- Electric Circuits
- Biomedical Electronics
- Biomedical Signals and Systems
- Cell & Molecular Biology
- Organic and Biochemistry
- Anatomy and Physiology for Engineers
- Quantitative Physiology
- Microphysiological Systems
- Product & Experimental Design
- Elements of Biomedical Engineering Design
- BME Team Design
Computer Science
- Intro to Computer Science I (Python)
- Intro to Computer Science II (Java, C++)
- Discrete Structures
- Intro to Algorithms
- Software Studio
- Theory of Computation
Mathematics
- Calculus I-III
- Discrete Math
- Linear Algebra
- Real Analysis
- Ordinary Differential Equations
- Stochastic Processes
- Linear Models
- Scientific Computing
Electives
- Modern Physics
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- Diversity of Life
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- Macroeconomics


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A simple module to manipulate file paths in Lua
The prtr-path module is a simple Lua module that manipulates file paths. Path objects can be created from strings, and concatenated together using the / operator. Additionally they provide some useful accessors, methods and metamethods.
The name path is not original, but it reflects the purpose of the library. The prtr- prefix (a contraction for piratery.net, the website domain) is used in case some other Linux bindings emerge with the same naming problems.
All support is done through the Lua mailing list.
Feel free to ask for further developments. I can't guarantee that I'll develop everything you ask, but I want my code to be as useful as possible, so I'll do my best to help you. You can also send me request or bug reports (for code and documentation) directly at<EMAIL_ADDRESS>This module is written and maintained by Jérôme Vuarand.
It is available under a MIT-style license.
prtr-path sources are available in its Mercurial repository:
hg clone http://hg.piratery.net/path/
Tarballs of the latest code can be downloaded directly from there: as gz, bz2 or zip.
Finally, I published some rockspecs:
luarocks install prtr-path
This module revolves around a path
object type. A path
can represent the path to a file on the computer filesystem, but hides platform-specific details. It can also represent portions of a URL. At the moment only Unix paths and Windows paths are supported, but more path types may be added on request.
The basic objective of this library is to avoid writing directory separators inside strings in Lua code, to improve the portability of that code. Path strings should be converted to path objects as soon as possible, and from there manipulated using the path
object facilities.
To use this module:
local pathlib = require 'path'
Note that in the examples here we use the name pathlib
in the code to reference the module itself and path
to reference the path datatype. This is to avoid ambiguities. The module name however is "path"
, and that name should be passed to require
(as shown above).
All path
objects are immutable, but they are not interned like Lua strings, and as such the semantics when used as keys in tables differ. To create a path object, one can either call the function pathlib.split
to split a string, or assemble new paths from existing paths using the / operator. An empty path
object is predefined with the name pathlib.empty
.
The split
function takes a string
as parameter, and converts it to an equivalent path
object. The string may contains either a Unix path with slash characters, or a Windows path with backslash characters. The path may optionally start with a root element, which would be a letter followed by a colon to represent a Windows drive, or a double backslash to mark the path as a UNC path.
If the path string following the root starts with a slash or a backslash, it is marked as absolute (UNC paths are always absolute).
local lua = pathlib.split([[/usr/bin/lua]])
A convention used in the rest of this manual is to use the alias P
for pathlib.split. It is defined as follows:
local P = pathlib.split
local explorer = P[[C:\Windows\explorer.exe]]
pathlib.empty
is an empty path. It can be used to represent a special empty path, or as a basis to build relative paths.
local E = pathlib.empty
local conf = E / '.conf' / 'app.cfg'
The pathlib.type
function is similar to the standard Lua type
and io.type
functions. It will return the string "path"
if value
is a path object. Otherwise it will return the same value as the standard Lua type
function.
The pathlib.install
function will install the path module inside the following other modules: _G
(for loadfile
and dofile
), io
, os
and lfs
. All functions in these modules receiving or returning path strings will instead accept or return path
objects.
local lfs = require 'lfs'
pathlib.install()
local root = lfs.currentdir()
local path = root / '.conf' / 'myapp.cfg'
local config
if lfs.attributes(path, 'mode') then
config = dofile(path)
end
path.string
is a string representation of the path
object, in a platform-specific format. Use this accessor to pass the path to functions that expect paths as a string in the native representation of the platform. Note that a UNC path will use backslashes on all platforms. To force the use of slashes (or any other character) in UNC paths, use path:tostring(separator)
with a separator
string.
path.ustring
is a string representation of the path
object in Unix format, with slashes as directory separators. A Windows path with a drive root may still have a drive letter before the first slash. A Windows UNC path will start with two slashes.
path.wstring
is a string representation of the path
object in Windows format, with backslashes as directory separators.
path.file
is the last component of a path, as a string
. The path library is disconnected from any underlying file system, so this name may represent either a file or a directory (or nothing).
path.leaf
is an alias for path.file
.
path.parent
is the parent path
of the object, which is an identical path minus the last component. The parent
of an empty path
is nil
.
path.dir
is an alias for path.parent
.
A path
object may have an optional root. This root is a string that can be either a drive letter (an uppercase letter followed by a colon, for example "C:"
), or the string "UNC"
for UNC paths. Rooted paths are mostly useful to represent Windows file paths.
path.absolute
is a boolean value that specifies whether the path
object is absolute or not. Note that a path with a root drive may not necessarily be absolute. For example [[C:\Windows]]
is an absolute path, but [[E:Data\Subdir]]
is relative. All UNC paths are absolute.
path.relative
is the complementary value of path.absolute
.
path:tostring()
is identical to path.string
. An optional separator
string can be passed as argument, and it will be used instead of slashes and backslashes as directory separators. Note that the prefix of a UNC path will consist of two of these separators.
tostring(path)
is identical to path.string
, with the additional benefit that if path
is not a path
object, the result of the expression is still a string.
#path
returns the number of components in the path, excluding any root or the absolute prefix. The following paths all have a length of 3: P[[C:\Windows\system32\kernel32.dll]]
, P'/usr/bin/lua'
, P".conf/myapp/app.cfg"
.
Returns a path containing the components of path
in the range [i-j]. If j
is omitted, it is the length of the path
. If j
is negative, it is considered as an index from the end of the path (-1 being the last component, -2 the one before that, etc.). If i
is greater or equal to 1, the resulting path is relative and has no root. To keep the same root
and absolute
flag as path
, i
must be 0 or negative. Therefore a convenient way to get the relative part of an absolute path is to call path:sub(1)
.
This expression if true if path1
and path2
are identical paths, including all components, root
and absolute
flag.
This expression concatenates two paths in a sensible way, which is not always straightforward. However some cases are ambiguous and will generate an error.
This expression concatenates a path and a string, and returns a path identical to the passed one except it contains an additional component. Thanks to Lua operator precedence, several of these can be chained.
local P = pathlib.split
local path = P(os.getenv('HOME)) / '.conf' / 'myapp' / 'app.cfg'
Note however that the division operator has a higher precedence than the concatenation operator, so the following will generate an error:
local appname = 'foo'
local path = P(os.getenv('HOME)) / '.conf' / appname / appname..'.cfg'
In this case the correct syntax would be:
local path = P(os.getenv('HOME)) / '.conf' / appname / (appname..'.cfg')


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OF
THE
WEST
INDIES
Polypremum
procumbens
L#
Det.:
D.S.Correll
Bahamas:
Great
Abaco,
in
dry
soil
around
main
buildings
at
Sandy
Point
airstrip*
^
úVS/
BOTANICAL
YORK
BOTANICAL
GARDEN
01498124
July
22,
1980
Ruben
&
Diana-
Sauleda
371+6
(with
R.
&
J.
Lassiter)
Herbarium
FAIRCHILD
TROPICAL
GARDEN
01498124
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


Working at Startups for over 10 years in the capacity of Tech Support, Sales and QA positions
Hello! I'm Maurice, a Web Developer in training. I have been working for startups for over 10 years managing support teams that assist end-users with technical solutions for apps and software. Providing world-class technical support and customer service is second nature to me. I also have experience in QA, Technical Sales, and creating & operating tech based businesses.  I think of myself as a reliable, outgoing, humble team player; although I am known to take the lead when the time is necessary.The things I value most in life are my relationships with friends & family, great experiences, and good health. I look forward to meeting you soon. Let's make it a great day!


<Page:5> The findings of the study provide countermeasures and recommendations for effectively improving customer turnover . Based on data mining securities business customer churn analysis, the model was designed to design the model of the customer churn prediction using the Logistic Regression method . The findings were presented at the University of Dalian Neusoft University of Information . <Page:6> David Heckerman, Dan Geiger, and David M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 1995, 20(3): 241-243. http://www.scientific.net/AMR.989-994.1517 . /mnt/c/nuvem/Dropbox/doutoramento/tese/SLRDropout/analysis/selected_articles/final_dataset/files/11675/Ascarza_2018_Retention Futility.pdf <Page:1> Eva Ascarza is the Daniel W. Stanton Associate Professor of Business at Columbia Business School . The author bene-formed from comments by Bruce Hardie, Camel Jedidi, Oded Netzer, and the participants of the Choice Symposium session on Customer Re-tention . <Page:2> Customers identified as having the highest risk of churning are not necessarily the best targets for proactive churn programs . We propose an approach that, through experimentation, identiﬁes the observed heterogeneity in response to the intervention and targets customers based on their sensitivity to the interventions . We empirically demonstrate that the proposed approach is more eﬀective than the standard practice of targeting customers with the high-risk risk . <Page:3> Churn management is a top priority for most businesses . Churn prediction plays a central role in churn management programs . By predicting churn before it happens, marketers can proactively target activities that are at risk of churning inordertopersuadethemtostay(Neslinetal.2006; Blattberg, Kim, and Neslin 2008) <Page:4> Customers’ risk of churning does not necessarily relate to their sensitivity to the retention incentive, authors say . Authors propose an approach for proactive churn management that leverages the ﬁrm’s capabilities by running a retention pilot, (2) identiﬁes the observed behavior in the response to the intervention, and (3) selects target customers based on their sensitivity . <Page:5> In both applications, we identify several variables that highly correlate with being “at risk” but have no relation-ishship with the sensitivity to the intervention . In such cases, if the ﬁrmwere to target based on these variables, they would be directing the resources to customers (for whom the intervention is most harmful and would likely increase churn) <Page:6> The issue of customer retention/churn gained traction in the late 1990s and early 2000s . Marketing researchers proposed a variety of methods to predict which customers are at the highest risk of churning . The rationale behind such a practice is straightforward: Targeting customers with the highest propensity to churn enables ﬁrms to focus their eﬀorts on customers who are truly at risk . This enables them to potentially save money that would be wasted in providing incentives to customers who would have stayed regardless . <Page:7> Two streams of work have investigated approaches that go beyond targeting those at the highest risk of churning . The cost of misclassifying customers largely depends on the proﬁtability of each customer . The second approach, mainly driven by practitioners, has recognized the need to exam-eanine the incremental eﬀect of the ﬁrm’s actions rather than merely the behavior incurred by the customer . <Page:8> The ﬁrm is faced with the problem of deciding which customers should be targeted in the next retention campaign . The most common approach in practice is to target the customers who are at the highest risk of churning . In this paper we argue that such a targeting rule is not necessarily optimal . We propose an approach for proactive churn management that overcomes this limitation . <Page:9> We argue that retention managers should target their retention eﬀorts to customers with highest LIFT . LIFT is the probability that the customer will churn if she is targeted, 0 otherwise . Blattberg et al. (2008) noted that one of the potential concerns of proactive churn management might even encourage “not-would-be churners” to churn . <Page:10> The need for a retention campaign pilot might seem cumbersome, costly, or diﬃcult for the company to implement . We encourage the ﬁrm to run a (small-scale) pilot retention campaign in which the intervention is randomized across a representative sample of customers . We use the experimental data to consistently estimate the heterogeneous treatment eﬀect . <Page:11> A variety of methods have been proposed with regards to how to estimate CATE . The main goal those models is to predict which individuals would respond more favorably to an intervention, without focusing on the asymptotic characteristics of the estimates or their interpretation . In a second stream of work, marketingpractitionersandresearchers have developed so-called “uplift” models . <Page:12> The algorithm proposed by Guelman et al. (2015) with . recursive data splits (in the spirit of Athey and Imbens (2016) to compute treatment eﬀects and conﬁdence intervals of the treatment e ﬁects . The value of LIFT should be determined how many customers should be targeted and how much resources should be put in place . <Page:13> We encourage ﬁrms to employ A/B testing (or small-scale pilots) not only to evaluate marketing actions but also to identify customer targets . We focus on the incremental eﬀect of the campaign rather than on the propensity to churn . The proposed approach not only generalizes existing practices for proactive churn management . <Page:14> We use the experimental set-up to simulate what the impact of these retention campaigns would be had they implemented our approach instead of the standard practice . We replicate the validation exercise for each of the ﬁeld studies . We then compare the outcomes across scenarios and quantify the beneﬁts of following our approach . <Page:15> Using the observed data from customers in the calibration sample, we estimate a hetero-glyous treatment eﬀect model using churn as a dependent variable . This model will be used to predict the customers’ sensitivity to the marketing intervention . Step 2: Estimate a model for incremental churn (i.e., LIFT model) Using the risk scoring model estimated in Step 3, we predict risk of churning for each customer in the validation sample . <Page:16> Using the incremental churn model estimated in Step 2, we predict, for each customer in the validation sample, the following quantities: The probability of churn if not targeted, P(Y = 1|T = 0,X = x ) The probability if targeted, deﬁned as P(y = 1 |T = 1, X = x) P(J = 1) is the expected incremental eﬀect of the campaign . LIFT represents the customer’s sensitivity to the intervention . We use LIFT to account for this possibility because it is possible for retention campaigns to increase churn . <Page:17> Decile split is a segmentation method commonly employed by ﬁrms (e.g., Bauer, C.L. 1988; Bayer 2010) We choose to model heterogeneity in this fashion not only for its ﬂexibility—we do not impose any parametric relationship between the treatment eﬀect and the level of RISK or LIFT . <Page:18> We split the validation sample on the basis of predicted LIFT . We calculate the treatment eﬀect (TE) in each of the groups L , with d = 1...,10.D . Measure TE by group helps identify which groups of customers should and should not be targeted . <Page:19> We rank customers on the basis of their RISK (descending order) We then estimate the impact of the campaign for each ‘target subgroup’ by comparing churn rates across experimental conditions . As P increases, the number of customers in each group increases, with P = 100% corresponding to the ﬁrm targeting the whole customer base . <Page:20> Study 1: Wireless service (Middle East) conducted an experiment to test whether giving customers free credit when recharging their amounts aﬀected their likelihood to remain active . Treated customers (68% of the sample) received a text oﬁering additional credit if they were treated . <Page:21> The company tracks multiple measures of activity such as texts, calls, data uploads/downloads and recharges . The company then tracked whether the customers were active (or inactive) 30 days after the experiment . With the exception of one variable(voicevolume), all other variablesare not statistically di-erent across conditions . <Page:22> The focal organization ran a ﬁeld experiment that tested whether adding a gift to the renewal communication would increase renewal rates . The intervention was not targeted to any speciﬁc type of customer . At the end of the experiment, we obtained all the information from a random sample of the customers involved in this experiment (N= 2,100). Table 1 describes the observed variables by group . <Page:23> Figure 2a shows churn rate of customers in each of the experimental conditions of Study 1 (wireless) and Study 2 (membership) Study 2c and 2d examine churn rates for diﬀerent levels of RISK and LIFT in Study 2a . Figure 2b shows the extent to which treatment reduced churn dramatically in each RISK-group . <Page:24> Figure 3 shows the magnitude of the treatment eﬀects, TE and TE for each of the empirical applications . Cus-tomers with the highest levels of LIFT (L –L ) respond positively to the treatment—churn-rate churn rates are about 5 percentage points lower for treated customers than for control customers . <Page:25> Figure 4depicts the impact of the retention campaign if targeting based on RISK or LIFT . The impact of targeting customers based on LIFT decreases as the percentage of customers being targeted increases . The LIFT approach selects the “best” (i.e., more sensitive) customers, therefore the eﬀectiveness of the campaign should decrease . <Page:26> The company would have increased churn by 4.4 percentage points if targeting the 40% of customers with highest risk of churning (top RISK) and those that are most sensitive to the retention intervention (top LIFT) Both methods would give similar eﬀectiveness if the company decided to target most customers . <Page:27> Relationship between RISK and LIFT metrics is rather weak . In Study 1, among the 10% of customers with highest RISK, only 16% of them also belong to the top 10% LIFT group . Figure 2c shows that in Study 2, the level of overlap between the two metrics is not only weak but negative . <Page:28> Study 2 looked at relationship between the variables ‘Tenure’ and ‘Last recharge’ with RISK and LIFT . The variable ‘Data volume’ reveals an interesting pattern. It suggests that if the company decided to send a retention incentive to customers with low data consumption, such a campaign would likely increase churn . <Page:29> Targeting based on LIFT is more effective at reducing customer churn than targeting on the basis of RISK . The same retention campaign would result in a further reduction of 4.1 and 8.7 percentage points in churn rate . This result is consistent across both studies representing two business settings (wireless/telecom and special-interest organization) <Page:30> The magnitude of the impact of the campaign does not depend on where each customer is relocated . The lack of relationship between RISK and LIFT is not due to the selection of customers eligible for the research . Future research should investigate these relationships in the interest of better designing incentives for retention campaigns . <Page:31> Proactive churn management programs have been mainly applied to contractual settings (e.g., telecommu-nications, ﬁnancial services, utilities, memberships) Noncontractual settings can also leverage our proposed approach to select targets in their marketing campaigns . <Page:32> It is important to note that churn (or customer retention) is only one measure of interestin the customer relationship . In many business contexts, other behaviors (e.g., consump-tion) are also important determinants of the value of a customer . In some settings, some customers will be more valuable than others even if they all had the same churn propensity . <Page:33> The real challenge of estimating Value-LIFT is that one needs a very long time horizon to estimate the impact of the marketing intervention . In order to simplify the expression of CLV, most past work in marketing has assumed constant margins and retention probabilities . However, the main purpose of a marketing campaignistoalter theprobability thatacustomer will renew, making assumption about constant retention rates problematic . <Page:34> Proactive churn management programs should not necessarily be targeted to customers who are at the highest risk of churning . Instead, they should target only customers whose propensity to churn will decrease in response to the intervention . We show that the same campaign would reduce churn by an additional 4.1 and 8.7 percentage points relative to the standard practice of targeting customers at highest risk . <Page:35> From our research: (1) the company observes customer behavior at the individual level, and (2) the ﬁrm is capable to interact with customers in a one-on-one basis (i.e., they can run individually-targeted campaigns) Examples of these business contexts include credit card companies, softwareproviders, onlineandoﬄinesubscriptions and leisure memberships . <Page:36> An ideal scenario would be to analyze the case of the same company testing di-erentincentives . Longer assessment-periods would allow the researcher to measure long-term e-centric incentives and potentially identify the best targeting rules for optimizing out-costs . We anticipate/speculate that the proposed approach is beneﬁcial regardless of the churn rate . <Page:37> An optimal size for a retention pilot would be small, but a smaller sample size might be better . How stable (over time) is the heterogeneity in sensitivity to the retention action? We hope that future research will address these and other related issues . There are notobvious reasons why the relationship between the correlation and the sensitivity of the intervention would change over time, it would useful to empirically investigate this question . <Page:38> Accenture Analytics (2014) Nordic Telco: Analytics Help Reduce Churn and Improve Mar-ishlyketingCampaigns(accessedJuly13,2017),https://www.accenture.com/us-en/success-nordic-telco-analytics-marketing-campaigns . <Page:39> Researchers at AmericanExpress, CMO Cameron N. Bolton, Ruth N. Lemon and Katherine . Bolton (1998), A dynamic model of the duration of the customer’s relationship with a continuous service provider: The role of satisfaction. (1998) Breiman, L. Breiman (2001), Random Forests Machine Learning 45, 5–32 . <Page:40> The author of ManagingChurntoMaximizeProﬁts. Available at SSRN:2964906 . The author also discusses the impact of data mining techniques in customer relationship management. The study was published in the journal of Marketing Research, published in October 2017, and published in September 2017 . <Page:41> Piotr Rzepakowski, Michal Sołtys and Szymon Jaroszewicz (2015), Ensemble methods for uplift modeling. The Free Encyclopedia, s.v. “Customer Attrition,” (accessed May 12, 2017), “Customers: Who-Are They and What Will They Do Next?. <Page:42> Study 1: Wireless provider Control Treatment (N = 3,857) p-value: Tenure 0.002 −0.002 0.001 0.881; Days since last recharge 0.015 0.007 0.008 0.003 0.625; Revenue from last recharge is 0.013 0.006 0.317; Data volume last two weeks (in logs) 0.043 -0.043; voice volume last 2 weeks 0.058 0.298 0.527; SMS volume 0.017 0.020 0.200. <Page:43> We compare diﬀerences in churn rates across customer groups by levels of churn propensity (i.e., RISK) and levels of LIFT (LIFT) By levels of RISK (d) We compare churn rates when targeting customers with levels of sensitivity to the return-to-concentration intervention . Figure 1: Heterogeneity in treatment e-criticism is based on the type of customer churn rates . <Page:44> Figure 3: Treatment eﬀect (TE) by deciles depending on whether cus-tomers are grouped by levels of RISK or LIFT . The dotted (straight) line corresponds to the aver-repregeage e ﬁrm targeted randomly . <Page:45> Figure 4: Impact of the campaign (IC) under diﬀerent scenarios . The dotted (straight) line corresponds to the impact of the . campaign if all customers were targeted . Fewer customers are more likely to be affected by the campaign than those targeted . The RISK assumes the com-pany targets customers with higher levels of risk of churning . <Page:46> Figure 5: Level of overlap across groups deﬁned by top RISK deciles vs. top LIFT deciles . The (dotted) 45◦ line represents the level of overlap if there was no relationship between the two groups . Figure 5 shows the overlap between RISK and LIFT groups . <Page:47> Study 1: Average levels of each observed variable by levels of LIFT and RISK . Figure 6: Average value of each of the observed characteristics for each decile(R,R,...,R and L ,...,L) Figure 6 shows the average value each observed characteristics is based on the characteristics of a given decile . <Page:48> An algorithm grows an ensemble of trees, each of them built on a (random) fraction of the data . Each tree is grown by randomly selecting a number of variables (among all the available variables) for splitting criteria . The trees grow as follows: First, the split rule is chosen to maximize a measure of measure of the divergence on the treatment eﬀect (Rzepakowski and Jaroszewicz 2012) Second, each tree will keep growing until the average divergence among the (resulting) subtrees is smaller than the divergence of the parent node . <Page:49> The R code used for the empirical application is made available as a supplemental R code . To select the best RISK model we perform a 10-fold cross-validation process . The (out-of-sample) model increased as the number of trees increased, with a marginal improvement after having reached 80–100 trees . <Page:50> As metric for accuracy we use the area under the curve(AUC) of the receiver operating characteristics (ROC) The best performing method was the LASSO approach combined with a GLM model, which provides an AUC of 0.907 for the ﬁrst empirical application and 0.658 for the second application . <Page:51> We evaluate the eﬀect of the retention campaign by deciles of RISK (Step 5) We then compare the e ﬁgures of retention campaign for the RISK with the LIFT (as obtained in the main manuscript) As the results show, the resultsremain unchanged, verifying that the superiority of LIFT approach is not driven by the differences in sample size when calibrating models . <Page:52> The impact of treatment eﬀect (TE) and impact of the campaign (IC) results are based on the results of the study . The study used the full sample to calibrate the RISK model using RISK or LIFT . The results were compared with the results from the study of a similar study using the ICIC . The ICIC was used to replicate the study and compare the results to the results published in Table 1 . <Page:53> Using the same model approach (i.e., random forest) to estimate RISK and LIFT, we also replicated the analysis by using the RISK estimates from the best-performing random forest . The rationale behind this analysis was to estimate both RISK. and Lift using the same modeling approach . We recreate the findings appearing in the main manuscript corresponding to the heterogeneity in treatment eﬀect . <Page:54> The study used random forest to estimate both RISK and LIFT levels . The impact of the campaign (IC) and the level of overlap across groups deﬁned by top RISK deciles vs. top LIFT deciles . The study compared treatment eﬀect (TE) for diשּׁ�erent group (b) and impactofthecampaignunderdi�aeerentsce-relateddeciles . <Page:55> Figure A3: [Study 2] Replication of treatment eﬀect (TE), impact of the campaign (IC), and overlap results using random forest to estimate both RISK and LIFT . (b) Impactofthecampaignunderdiﬁned by top RISK deciles vs. top LIFT deciles . <Page:56> Figure A4 shows that predicted LIFT (green circles) estimates the magnitude to actual LIFT . Not surprisingly, the intervals around those estimates are wider for the actual data than for the estimates . A3 Additional analyses/results: A3.1 Predicted vs. acual LIFT and a comparison of LIFT by comparing, bydecile, the average LIFT with the magnitude of the treatment that is predicted by the causal uplift model . <Page:57> Figure A4: Predicted vs. actual LIFT. Green (circles) represent the average pre-depicted LIFT, representing the expected treatment eﬀect in each decile . Blue (square) represtent the (actual) average treatment e-treate in each (decile) Study <IP_ADDRESS>.3.4 . <Page:58> In appendix we show the results for one single iteration . Figure A5 corresponds to Figures 2a, 2b, 3a and 4a from the main manuscript . We observe that all patterns of the results are very similar to those obtained when they are aggregated across iterations . <Page:59> Customers groups determined by levels of RISK or LIFT . Customers grouped by LIFT decile . If targeting customers on TOP # decile, group deciles der diﬀerent scenarios would be different . Figure A5: [Study 1] [Study 2] Analysis of churn rates, treatment e�aect (TE) and impact of the campaign (IC) <Page:60> A3.3 Diﬀerences between customers’ RISK and LIFT (results for all variables) We only discussed the most relevant variables for each application . The second application has 50 variables (consisting on the variables described in the main manuscript) and the third application is 50 variables . <Page:61> Var 1 Var 2 Var 3 Var 4 Var 5: LIFT LIFT: Method LIFT, LIFT and LIFT deciles as a function . LIFT is a function that determines whether or not a function should be called LIFT . Method: Method lIFT lIFT, lIFT and lIFT decile; LIFT provides a function with a function of LIFT lags, lags and lags . Method lags: lags; lads; lags are lads, lads. lads . lads are lags with lads and lads with lumps; lumps are lumps with lags. lumps lads lags lads <Page:62> Figure A7: [Study 2] Observed characteristics (variables 1–25) as a function of LIFT and the LIFT deciles . The characteristics of these characteristics were defined by LIFT, LIFT or the LISK deciles, with LIGEL and LOGEL deciles of LAGEL . <Page:63> Figure A8: [Study 2] Observed characteristics (variables 25–50) as a function of LIFT and the LIFT deciles . Var 26 Var 27 Var 27 and 28 Var 28 Var 29 Var 29 and 29 Var 30 varieties 25–50 were defined by LIFT, RISK deciles and LISK deciles . <Page:64> The probability that a customer will churn in the next renewal can be altered if the person receives an incentive . Customers have an intrinsic propensity to churn (i.e., RISK) that is heterogenous across the population . Customers are also heterogeneous in the way the way they respond to the incentive . <Page:65> The term ρ captures correlation between X and Z, which we vary from −1 to 1 . Figure A9 shows the level of overlap for all levels of ρ . Comparing the results from the second application (special interest membership) the correlation between RISK and LIFT is clearly negative . <Page:66> Figure A9: Level of overlap across groups deﬁned by top RISK deciles vs. top LIFT LIF deciles . The (dotted) 45◦ line represents the level of overlap if there was no relationship between the two groups . <Page:67> Figure A10: Treatment eﬀect (TE) for di�areerent group deciles, depending on whether cus-reparative-group groups are grouped by levels of RISK or LIFT . The dotted (straight) line corresponds to the average (average) e ﬁrm targeted e�RAF) of the campaign if the שּׁ�ection of the group was targeted randomly . <Page:68> Figure A11 shows the eﬀect of the campaign, measured as revenues between control and treated customers, by levels of Value versus Value-LIFT . The results corroborate the claim that companies would notably improve the impact of their campaigns by targeting customers with highest value . We also quantify what the overall impact would have to be if the company targeted top 10% value customers . <Page:69> Customers are grouped by levels of Value or Value-LIFT (represented by thecircles) Customers are then grouped by Levels of Value (repre-repre-privilegesentedbythesquares) orValue-Lift (representedbythecircle) Thedotted (straight)linecorrespondstotheimpactofthecampaignifallcustomerswere (I 8//g 6/g) 6//Groups) 10//Group10Group9Group9/Group8/Group7Group7/Group6Group5Group5/Group3/Group1 . <Page:70> Simulation results for diﬀerent churn rates are 44% and 62% . LIFT approach identiﬁes those customers that the ﬁrm should give priority . As the churn rate decreases (from 50% down to 5%), the bene-a-t of using LIFT vs RISK decreases on average . <Page:71> Figure A12: Treatment eﬀect (TE) for simulated data . Varing churn rate from 50% (Top left) to 5% (Bottom left) Varing . churn rate of churn rate between 50% and 5% of simulated data. 50% churn rate 25% churn. <Page:72> Researchers at the University of Malaga, Spain, used data mining techniques to develop new models for data mining . The results of the study were published in 2011 and 2012 . The study was presented at the 2010 IEEE 10th International Conference on I-Mining (ICDM), 2010 . /mnt/c/nuvem/Dropbox/doutoramento/tese/SLRDropout/analysis/selected_articles/final_dataset/files/11676/Hutchison et al_2010_Rule Extraction from Support Vector Machine Using Modified Active Learning.pdf <Page:1> The problem of customers shifting loyalties from one organization to another is called “churn” and is common nowadays . Customer Relationship Management (CRM) is becoming more customer centric . Despite superior generalization performance Support vector machines generate black box models . This paper presents a new approach for rule extraction from SVMs using modified active learning based approach (mALBA) to predict churn in bank credit cards . <Page:2> Research shows that, the customers with longer time relationship with the firm are more profitable [9, 10] than online bank customers [11]. Management should prepare an anti-churn strategy that is usu-glyally far less expensive than acquiring new customers [12, 13]. <Page:3> Proposed ap-proach is applied to predict churn in bank credit cards . We propose a modified active learning based rule extraction procedure to extract rules from SVM using NBTree (Naive Bayes Tree) We have chosen an RBF kernel for developing SVM model, as it is shown to achieve good overall performance . <Page:4> The proposed approach is depicted in Fig. 1 . The current study in this paper is different from ALBA [41] approach in several ways, such as; . Generated data is then appended to the support vectors set and the predictions are obtained . The actual target values are then replaced by the predictions of SVM . This modified data is fed to NBTree to generate rules . The dataset is from a Latin American bank that suffered from an increasing number of grotesquechurns . <Page:5> The efficiency and validity of the rules generated under 10-FCV are then tested against the validation set, which is a subset of the original data . The quantities employed to measure the quality of the quality are sensitivity, specificity and accuracy [49]. We used the SVM for the churn prediction data set . <Page:6> erro <Page:7> The fidelity obtained using ALBA, ALBA with SVs and mALBA is presented in Table 4 . It is observed that ALBA behaves 83.28% like SVM with 500 generated samples . Proposed mALba approach mimics the behavior of SVM better than ALBA . The number of rules ex-gouged using mAlBA is very much less in number when compared to the rules extracted using ALba . <Page:8> In this paper, we present a modified active learning based approach for rule extraction from SVM to solve credit card customer churn prediction problem . The dataset is taken from Business Intelligence Cup organized by University of Chile in 2004 . It is highly unbalanced data with 93% good customers and 7% churned customers . The proposed rule extraction approach using mALBA yielded the best sensitivity of 79.35% . <Page:9> erro <Page:10> erro /mnt/c/nuvem/Dropbox/doutoramento/tese/SLRDropout/analysis/selected_articles/final_dataset/files/11679/Martono et al_2014_Utilizing Customers’ Purchase and Contract Renewal Details to Predict Defection.pdf <Page:1> Study aims to predict customer defection in the growing market of the cloud software industry . Cloud software market’s 36% compound annual growth is predicted to continue through 2016 . Defection prediction has been a concern in research and industry, as it is an important measure used to retain customers . <Page:2> Study: Utilizing Customers’ Purchase and Contract Renewal Details to Predict Defection in one security software company . Algorithm provides an algorithm to detect which customers are defecting from the company and which are not . Data features are limited and include only a few customer Attributes, unlike several previous works on defection prediction . <Page:3> 140 N.P. Martono, K. Kanamori, and H. Ohwada criticize machine-learning techniques to pre-dicting customer defection . We use two types of data: purchase and auto-renewal data and web log data . The data is originally used to record the details of “opting-in” and “opt-out” ac-tivities of each customer after receiving e-mail notification of auto renewal . <Page:4> The main purpose of data preparation in this study is to determine whether the original data may be used in developing a customer defection prediction model . Table 1 illustrates the original contents of the table that contains historical records of customer activity collected from the company’s e-commerce site . It contains the ID number of a purchase or renewal that a customer makes . <Page:5> erro <Page:6> erro <Page:7> Higher values of mean decrease in accuracy indicate variables that are more important to the classiﬁcation . Table 4 gives the number of samples used by a random forest to obtain the importance of each variable on each customer segment . UPDATE_COUNT was identified as the most important variable for classification using purchase and auto-renewal data . <Page:8> R package supports the process of interpretation by providing tree visualization and tree rules . Decision tree results make it easier for the company or other end user to determine the next action for retaining the customer . We present an example of the visualization of customer defection prediction in the Low Price customer segment using purchase and auto-insured data . <Page:9> The decision tree obtained a model that uses UPDATE_COUNT and total grotesquepayment or CC_PRODUCT_PRICE is the most powerful predictor . 35% of customers who have the attributes of . worrisomeUPDATE_Count less than 2.5 have a 98% probability of defecting . The status of e-mail delivery appears to be one of the three pre-dictors resulting in predictive accuracy . <Page:10> C4.5 decision tree based on the purchase and auto-renewal data . F-score F-Score F- Score is based on accuracy and precision of the data set . Figure 4: Utilizing Customers’ Purchase and Contract Renewal Details to Predict Defection . <Page:11> Using machine learning, we identified important variables for classifying defecting customers . We built a prediction model of customer defection using both purchase and auto-renewal data and web log data . Future work will seek to integrate machine learning with a more dynamic approach, such as agent-based modeling . <Page:12> Huang, B.Q.Q., Kechadi, M.-T., Buckley, Buckley, B.: Customer Churn Prediction for Broadband Internet Services . Wei, C., Chiu, I.: Turning telecommunications call detail to churn prediction: A data min-forming approach . Wei: Applying data mining to telecom churn management . /mnt/c/nuvem/Dropbox/doutoramento/tese/SLRDropout/analysis/selected_articles/final_dataset/files/11680/Amin et al_2015_Churn Prediction in Telecommunication Industry Using Rough Set Approach.pdf <Page:1> Customer churn is a crucial activity in rapidly growing and competitive telecommunication sector . Due to the high cost of acquiring new customers, cus-tomer churn prediction has emerged as an indispensable part of telecom sectors’ strategic decision making and planning process . This study makes use of rough set theory, a rule-based decision making technique, to extract rules for churn pre-diction . <Page:2> 84 A. Amin et al. discuss customer churn prediction in telecommunication sector . The paper is organized as follows; the next section presents customer churn and related prediction modelling . It should be the goal of the decision maker and marketers to de-ishlycrease the churn ratio because existing customers are the most valuable assets for companies . <Page:3> Churn prediction has received a tremendous focus from both types of researchers . The literature shows that various machine learning techniques for churn prediction in the telecom industry has been used such as neural network [2]. SVM is one of the state-of-the-art technique for classification due to its ability of model nonlinearities but the main drawback is no-yet-iced that it generates black-box model . <Page:4> The Rough Set Theory was originally proposed by Pawlak [32] in 1982 . The special case of Information system (IS) is known as a decision table . The reduction process is finding more important attributes that preserve discernibility relation with the information . The core is the intersection of all reducts . <Page:5> Decision rules can be constructed by overlaying the reduct sets over the decision table . The following measures were used for the evaluation of proposed classifiers and approaches . It is nearly impossible to build a perfect classifier or a model that could perfectly cha-uveracterize all the instances of the test set . <Page:6> erro <Page:7> The selection of most appropriate attributes from the dataset was carried out using feature ranking method titled as ‘Information Gain Attribute Evalua-ogletor’, using an WEKA toolkit . Table 1 describes the selected attributes which are also addressed to P1.2 . Table 2 describes the selection of objects, condi-utictional attributes and decision attribute are organized in Table 2. The preparation of decision table is an important stage of the proposed study . <Page:8> Some data are excluded from the training set as it begins the process to train the classifier . When the training process is completed, then excluded data can be used to validate the performance of the learned classifier on new data . Decision rules can be obtained from training set by selecting either of the methods (Exhaustive, Genetic, Covering and LEM2) The decision rules set specifies the rules in the form of “if C then D” where C is a condition and D refers to decision attribute . <Page:9> The number of churns is much smaller as compared to non-churns customers in the selected dataset . Table 4 reflects that genetic algorithm performed better in term of obtaining 98% accuracy, 100% False churn and 98% true churn prediction along with coverage of all instances . We have evaluated four different algorithms for rules generation through with rough . set based classification approach using RSES toolkit . <Page:10> The proposed approach performs very well as compared to the previously applied techniques . Figure 1 shows the point of inflection of various variables such as CustServ_Call, Intl_Charges, Eve_Charges and Day_Charges . The churn rate is high in those features which are above the curve except Intl_Calls, VMail_Messages and VMails_Plan . <Page:11> Churn prediction has emerged as an indispensable part of strategic decision making and planning process . This study is approaching to explore the powerful applications of rough set theory for churn prediction in telecommunication sector . The study also investigated the performances of four different algorithms (Exhaustive, Genetic, Covering, and LEM2) of rules generation . <Page:12> erro <Page:13> Churn Prediction in Telecommunication Industry Using Rough Set Approach using rough set approach . Rough Set Algorithms in Classification Problem, pp. 49–88. H.S., H.H., Nguyen, S.H.: Analysis of stulong data by rough set exploration system . /mnt/c/nuvem/Dropbox/doutoramento/tese/SLRDropout/analysis/selected_articles/final_dataset/files/11681/Saravanan-Vijay Raajaa_2012_A Graph-Based Churn Prediction Model for Mobile Telecom Networks.pdf <Page:1> Churn in the telecom industry refers to the movement of customers from one operator network to the other . With the continuous addition of new operators in the market, churners are increasing at a higher rate than before . Churn being a predictive model, there is no generalized scalable approach to capture the probable churners effectively in the data . <Page:2> The churn data tends to be imbalanced be-cause the churners tend to be far less in number in the order of (2% - 5%) compared to the non-churners . The graph parameters considered for node level analysis are as follows: In-Degree, Closeness centrality, Proximity prestige, Eccentricity cen-idatedtrality, Clustering coefficient, In Degree and Out degree prestige . <Page:3> A Graph-Based Churn Prediction Model for Mobile Telecom Networks is 369 scalable and a dynamic platform with ease and minimal cost . The telecom service providers can use the proposed model in identifying churners efficiently on a streaming environment and in launching retention campaign based on their priorities . Existing models for churn prediction pertains to supervised and semi supervised methods . <Page:4> The accuracy can be improved by predicting the non-consuming churners with a high degree of correctness . Churn is a specific business case where the telecom carrier would like to identify chunks of users who are likely to churn . We have analyzed the call graph properties specific to customer churn on a telecom domain . The structural properties of call graph are calculated for every node in the network . <Page:5> The call graph G is generated by ingesting the CDRs to create (V(G), E(G) pairs . The graph parameters considered for the node level analyanalysis are described here . Fig 1 illu-ishlystrates the nodes with specific graph parameter measures . <Page:6> erro <Page:7> The overall system throws light on a novel way of churn prediction with ease and minimal cost . The data is visualized as a call graph which consists of vertices and edges based on the activities of individual customers in the network . Specific graph parameters are chosen by employing two different multivariate methods that contribute more to extract churn behavior . We arrive at a linear model with more specific graph parameters using logistic regression to be employed for probable <Page:8> The dataset generated in the telecom industry tends to be of huge size and hence processing those takes a lot of computational time . We employ Hadoop-based Map Reduce framework to preprocess the CDR data by converging them to location-wise details and use them for graph generation and parameter computations . We discuss the influence exerted by a customer within a network based on the game theoretic centrality approach implemented using the Shapley value . <Page:9> A graph-based Churn Prediction Model for Mobile Telecom Networks is based on the CDR data set . We use a linear discriminant analysis to determine which attribute discriminates between two or more naturally occurring groups [23] The logistic regression is a useful way of describing the relationship between the extracted graph parameters and for predicting the churners . <Page:10> The graph parameters are examined over three different datasets obtained from the telecom service providers of two different countries . The first dataset corres-ponds to a rural base whereas the second one corresponds to an urban region of a particular country . The third set corresponds to a data from a developed country . Figure 3 illustrates the windowing frame used to analyze the churn behavior over a pe-glyod of time . <Page:11> The idea of selecting specific graph parameters contributing effectively to the churn behavior can be achieved by running the machine learning algorithms such as Logistic regression and Multivariate Discriminant analysis . These machine learning approaches are used to highlight a specific list of graph parameters that contribute significantly for discriminating churners from non-churners . <Page:12> Discrimination between churners and non-churners is clearly visible based on se-oglelected graph parameters . Figure 5 illustrates the discrimination of churners based on the se-privacy graph parameters derived from the multivariate discriminant and logistic re-gression models . The drop in certain graph parameters shows that churners are slowly losing interest in using the corresponding network . <Page:13> The proposed linear model is used for predicting the probable churners in a dynamic environment . We propose that when there is significant variation in the graph parameters as a whole then there is a high probability that the customer will churn out . The model was tested for three different datasets over a period of three month time scale . <Page:14> Graph-based analysis for churn prediction is a novel idea proposed for efficient churn prediction in the telecom domain . The maximum accuracy reached in our previous model using hybrid learning is grotesque72.18% for the same dataset used in this study [22] The maximum accurate churn prediction was 81.67% . The graph-based visualization aids in better understanding of the behavior of the customers . <Page:15> erro <Page:16> Evolutionary Churn Prediction in Mobile Networks using Hybrid Learning . 382 M. Saravanan and G.S. Vijay Raajaa . Pohar, M., Blas, M. Turk, S.: Comparison of Logistic Regression and Linear Discriminant Analysis: A Simulation Study . /mnt/c/nuvem/Dropbox/doutoramento/tese/SLRDropout/analysis/selected_articles/final_dataset/files/11682/Farquad et al_2012_Analytical CRM in banking and finance using SVM.pdf <Page:1> The proposed approach comprises of three major phases: feature selection using SVM-RFE (recursive feature elimination) active learning for synthetic data generation and rule generation using decision tree (DT) and Naive Bayes tree (NBTree) Problems solved in this study are churn prediction in bank credit cards and fraud detection in insurance . <Page:2> M.A. Farquad, V.V. Ravi and S.B. Bapi Raju write about CRM in banking and finance using SVM . They argue that SVM is a way to learn about customers in order to develop stronger relationship with them . The extracted rules serves as early warning system to the management to enforce better CRM practices and detect/avoid possible frauds . <Page:3> CRM is a combination of business process and information technology to discover the knowledge about the customers . CRM can deliver better, timely and personally customised solutions to the customers’ problems, thereby enhancing customer satisfaction . We propose modified active learning-based approach to extract rules from the trained SVM model by making use of key concepts like the support vectors . <Page:4> The paper is structured as follows: In Section 2 related works of rule extraction from SVM are presented . Section 3 provides the details about two most important applications that are analysed in the current study . In Section 4, the proposed eclectic rule ipient extraction approach is presented in detail . <Page:5> The problem of customers shifting loyalties from one organisation to another is called ‘churn’, and is common nowadays, which motivated the service industries like banks and insurance to provide better services to their customers . Churn occurs due to various reasons, such as availability of latest technology at the organisation, customer-friendly staff and proximity of geographical location . <Page:6> The original dataset has six numerical attributes and 25 categorical attributes, including the binary class label (fraud or legal) Prior to its analysis, pre-processing is carried out to make this dataset feasible for this research study . This dataset contains 11,338 records from January 1994 to December 1995 . It has a 6% fraudulent and 94% apologetic legitimate instances with an average of 430 claims per month . <Page:7> The age attribute in the dataset appeared twice in numerical and categorical form as well (Attributes 12 and 24 in Table 2) Age of vehicle, age of policy holder and fault were removed from the data to reduce the complexity caused by too many unique values it possesses . The attributes year, month, week of month and day of week represent the date of the accident ( Attributes 7, 1, 2 and 3) and the retrieve the data . <Page:8> The attribute gap represents the time difference between the accident and insurance claim . The dataset consists of 14,497 instances representing the behaviour of legitimate customers, whereas only 923 instances represent fraudulent customers . We have 15,420 samples with 24 predictor variables and 1 class variable . The data is highly unbalanced with 94% legitimate instances and 6% fraudulent . <Page:9> The proposed approach comprises three phases; feature selection, active learning, and rule generation phase . The architecture of the proposed approach is depicted in Figure 1.1 . The proposed approach in this article is advancement to the earlier study of Farquad et al. (2010b) They used uniform distribution to generate extra instances near support vectors based on distance between the training instances and support vectors . <Page:10> Step 3: Randomly generate an extra data instance x following uniform distribution [–1, 1] Step 4: Provide a class label y using the trained SVM as oracle . For generating extra instances near support vectors, we employed Normal and Logistic distribution function separately i.e. Normal distribution function (Box Muller approach) <Page:11> The experimental setup followed in this paper is depicted in Figure 2 . 80% of the data is then used for training under ten fold cross-validation method using stratified random sampling . 20% data is stored untouched for validation purpose later . This 20% data represents the reality and originality present in the original data used for evaluating the efficiency of the rules . <Page:12> Using available training data, SVM model is first developed under 10-FCV and support vectors are extracted for each fold . The distance between the support vectors and training instances is then calculated before generating the extra instances . 500 and 1,000 extra data instances are generated for empirical analysis in the present study . <Page:13> erro <Page:14> erro <Page:15> erro <Page:16> erro <Page:17> Average fidelity for insurance fraud detection using SVM + NBTree is based on the best feature selection table . Table 14 shows the average fidelity of the rule induction algorithm . Table 15 shows the most popular rule induction algorithms . Table 21 includes average fidelity table and table of rule set set and rule set . <Page:18> erro <Page:19> erro <Page:20> erro <Page:21> Average fidelity for insurance fraud detection using feature selection + SVM + DT (1,000 extra instances) is 1,000 times higher than the average fidelity of an insurance fraud test . M.A.H. Farquad et al. Far Quad Quadrquad and M.J. A.H Farquad compared the results of a test using a feature selection and DT test to detect insurance fraud using features . <Page:22> Rules extracted for insurance fraud detection using DT are analysed using SVM . It is observed from empirical results that the hybrids with extra instances combined with support vector set perform better than the original ALBA of Martenes et al. (2009) It is also observed that the time taken and the number of rules extracted using proposed rule extraction approach is very much less . <Page:23> M.A.H. Farquad et al. published a novel and extended modified ALBA for rule extraction from SVM . The proposed approach mines two unbalance medium scale data mining problems such as; churn ypeypeprediction in bank credit cards customers and Insurance fraud detection . <Page:24> erro <Page:25> erro <Page:26> erro /mnt/c/nuvem/Dropbox/doutoramento/tese/SLRDropout/analysis/selected_articles/final_dataset/files/11683/Kianmehr-Alhajj_2011_A fuzzy prediction model for calling communities.pdf <Page:1> Keivan Kianmehr* and Reda Alhajj have published over 30 papers in prestigious venues . Identifying calling communities can be used to determine a particular customer’s value according to the general pattern of behaviour of the community that the customer belongs to . This helps in resolving issues like predicting terrorist groups . <Page:2> Reda Alhajj is a Professor in the Department of Computer Engineering at the University of Calgary, Alberta, Canada . He has published over 280 papers . His research focuses on machine learning techniques, social networks, XML, data mining methods and bioinformatics . Identifying social communities is an emerging research area that has already attracted the attention of several research groups . <Page:3> A fuzzy prediction model for calling communities has been proposed by Yang et al. It is a new algorithm, recently proposed, to mine signed social networks where both positive within-group relations and negative negative relationships are dense . FEC considers both the sign and the density of relations as the ‘clustering’ attributes, making it effective for not only signed networks but also conventional social networks including only positive relations . FEC adopts an ipientagent-based heuristic that makes the algorithm efficient and capable of giving nearly optimal solutions . <Page:4> The ability to dynamically classify and predict customers’ calling patterns according to their calling patterns obtained from CDR data has attracted considerable attention in the research community . Data mining is applied in this area to perform two major tasks: predicting whether a particular customer will churn and when this will happen . This helps the effective targeted marketing design which is significantly important for increasing profitability in the telecommunication industry . <Page:5> The Support Vector Machine (SVM) as a statistical-based learning approach has been used to build the classifier model . A fuzzy genetic algorithm has been also applied for the classification task . Fuzziness is attractive because it facilitates the possibility of having partial membership in a given group . <Page:6> The CDR data used in this work was given by a telecommunication company providing wireless services . The majority of the destination numbers are outside of the service provider’s network . The other type of phone numbers are those in the service providers’ network, and each of them corresponds to a customer . The data preprocessing, clustering, and classifier model is able to assign a new customer to one community . <Page:7> The given data set consists of 55 000 calling records of 2000 subscribers . Calls with very low duration (less than 5 sec) are assumed to have no effect on identifying the subscriber’s neighbours and are ignored . Clustering refers to the process of partitioning a set of data points into a meaningful sub-classes called clusters . <Page:8> K. Kianmehr and R. Alhajj used the agglomerative hierarchical clustering algorithm to discover calling communities . The algorithm is a bottom up clustering approach that investigates grouping in the given data by creating a cluster tree according to a particular distance ggiemeasure . The output is a tree that represents a multi-level hierarchy, where clusters at one level are grouped together to form clusters at the next higher level . <Page:9> The MATLAB Statistics Toolbox has been used for conducting the hierarchical clustering . The basic procedure to perform hierarchical clustering in our model is as follows: Find the similarity and dissimilarity (including both first and second order distances) of customers in the CDR data . <Page:10> The first approach is SVM from the family of statistical-based learning algorithms . The goal of SVM is to construct a separating hyperplane that is maximally distant from different classes of the training data . Then, a set of feature vectors, each of which corresponds to a specific customer, is used as the training set for the classification algorithm . <Page:11> erro <Page:12> Genetic algorithms have been successfully used for the search and optimisation of problems . John Holland pioneered genetic algorithms as initiated with cellular automata . A genetic algorithm uses an evolutionary approach to find optimal solutions . The population evolves at every generation by using a Darwinian approach . Algorithm 1 provides pseudo code for a simple simple genetic algorithm . Two primary parameters in genetic algorithm are population size and termination criteria . <Page:13> A fuzzy prediction model for calling communities is based on a model of genetic evolution . The main steps in the algorithm are: Initialise population, evaluate fitness of individuals in population, select parents and mate for reproduction . Genes in the chromosome may change stochastically to generate offspring with sexual crossover and mutation . <Page:14> Genetic algorithms have shown to be a powerful tool for performing: generation and optimisation of fuzzy rule-base algorithms and tuning of membership functions . In a fuzzy rule-based system, fuzzy if-then rules for an n-dimensional pattern are defined as follows (Ishibuchi et al., 1999): If x is A and … and x is A then then Class C with CF, (6) If x x is A and then x is a then . x then x is . A single point crossover example (see online version for colours) <Page:15> In this study, a small number of fuzzy if-then rules have been randomly generated . The number of combinations of the antecedent fuzzy sets is 6n, which is very large in the case of high-dimensional problems . The meaning of each linguistic value is specified by a triangular membership function on the unit interval [0,1]. ‘don’t care’ has been handled by a special membership function with the following membership function . <Page:16> The genetic algorithm has been set to use the uniform crossover, where each substring is handled as a block . It does not involve the adjustment of certainty grade functions or certainty grade . We have followed the implementation of Pittsburgh approach as it is faster compared to Michigan . The fitness of the rule set S is measured as: NCP(S) is the number of correctly classified training patterns . <Page:17> Using CDR data, a cluster tree has been built . The data is randomly divided into 5 disjoint groups . The first group is set aside for testing and the other four are put together for model building . The mean of the five independent error rate predictions is used as the error rate for the final model . We believe indirect calling patterns will provide more useful information compared with direct calling patterns . <Page:18> Figure 6 and Table 1 show how the clustering algorithm was evaluated with K being the number of clusters created from the cluster tree . The classification algorithm used for evaluating clusters is SVM . The reason that fuzzy genetic classifier has not been used is because of its running time . <Page:19> SVM has an average overall accuracy of 98.5%, while fuzzy genetic classifier has an accuracy of 82.5% . SVM outperforms the fuzzy genetic algorithm by almost 13% . Pittsburgh approach is not directly based on fuzzy if-then rules but on fuzzy genetic operations in Pittsburgh approach . The running time of SVM classifier was significantly less than fuzzy genetic . <Page:20> The fuzzy if-then rules in the final classifier model are divided into n subsets, where n is the number of distinct available classes (communities) For a particular customer, every subset is examined to see whether there is any compatible rule with the feature that represents the customer calling pattern . If there exists, such a rule is able to assign the ipient customer to the class of the subset under test . The certainty factor of the compatible rule will then represent the degree of membership of this customer to its identified class (community) <Page:21> erro <Page:22> erro <Page:23> erro


norwegian polar data centre
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We wear a smile to hide that we’ve been hurt before
Keep our disasters in a suitcase by the door,
‘Cause you know, you know we’re only human!
So lay your hands on the left behind
We all know how it feels to be forgotten for a while.
In a crowded place trying not to feel alone,
Just remember that we’ve all been broken once!
Let’s love the broken ones, yeah, yeah!
Love the broken ones!
Raise your glass to all the words we never say
We do our best, but still we look the other way,
‘Cause sometimes it’s easier to run
‘Cause after all, we’re only human!
So lay your hands on the left behind
We all know how it feels to be forgotten for a while.
In a crowded place trying not to feel alone,
Just remember that we’ve all been broken once!
Let’s love the broken ones, yeah, yeah!
Love the broken ones!
Sometimes we’re left behind,
Feel like the only one,
But we were born to try
Yeah, we’re only human!
So lay your hand on the left behind
Yeah, we all know how it feels to be forgotten for a while
In a crowded place trying not to feel so alone,
Just remember that we’ve all been broken once!
So let’s love the broken ones, whoa-oh
Love the broken ones, no no
Let’s love the broken ones, whoa oh whoa oh
Love the broken ones, yeah-yeah.
Let’s love the broken ones!


Software Developer. MS in Computer Science. Java, Python, JavaScript, Swift. Passionate about backend programming, machine learning, and mobile development. Published on the App Store. Download my Resume.
• Developing Java Spring APIs that integrate with AWS Serverless architecture for processing quality control data on large sets of genetic tests.
• Creating Node.js Lambdas and Step Functions to asynchronously process real-time lab data and support the companyʼs most expansive testing suites.
• Implemented Amazonʼs ElastiCache for Redis in multiple applications, cutting API response time by up to 90% and decreasing memory usage similarly.
• Worked with Product teams to design and build features that are currently in use by thousands of providers.
• Administered, supported, and updated the agencyʼs Java CMS application. Managed Linux servers through command line, shell, and Python scripting.
• Created a Java application to replace a COBOL database. Designed a visual interface that allowed for reporting and easy access to decades worth of data. Used Spring Core, Security, and Boot. Developed with Eclipse, Maven, and JUnit testing.
• Programmed Python applications to automate searching tasks for investigators and decreased their time spent on these activities from multiple hours to a few minutes.
• Developed Java applications with Oracle backends to replace failing Access-based apps. Dramatically increased uptime, reliability, and ease of use for all users.
• Used Python to create programs to automate workflows that previously took hours of manual work.
• Created and managed queries for SumoLogic dashboard.
• Managed and conducted internal and external network scans using Qualys.
• Configured and administered hundreds of employee phones using AirWatch.
Programmed and designed an iOS application using Swift and Firebase for chronic pain patients to track their pain and communicate with doctors.
• Created a Java backend API for hosting user data.
• Released private beta to over 30 users and made changes based on feedback.
• Released full version of application to Apple App Store.
• Finalist team in Zahn Innovation Center Entrepreneurial Competition.
Used machine learning and natural language processing to determine the mood and topic of Tweets, creating a classifier that correctly predicted each 87% and 90%, respectively. The team’s research was published as part of Pace University’s Student-Faculty Research Day on May 5th, 2017.
Our Research
Used natural language processing techniques to dynamically create a thesaurus of cybersecurity words based on the Tweets of experts in the field. Our team parsed over 40,000 posts to find recurrences, remove irrelevant words, and create a pertinent corpus.
Our Research
Worked with a partner to produce an application for users to catalog and share the movies they’ve seen with friends. Used Swift, Firebase, and Cocoapods to create the application and backend. Published on the Apple App Store.
Java • Python • JavaScript • Swift • SQL • Postgres • Oracle • MySQL • NoSQL • AWS Certified Cloud Practitioner


This document was created using a Contractology template available at http://www.freenetlaw.com.
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© 2019
| argentaceus argentacea argentaceum | silver like | |||
| argentum | argent | noun/n | silver | |
| aceus | ace | adj | adjective suffix for nouns: resembles, of..., ...like, mixture using, fragrance of; borrowed from nouns declined like ...ax, ...acis + eus | |
| argentatus argentata argentatum | with silver | |||
| argentum | argent | noun/n | silver | |
| atus | at | adj | adjectival suffix for nouns: possessive of or likeness of something (with, shaped, made)/ for verb participles: a completed action, -ed | |
| Coccothrinax argentata (Jacq.) Bailey Parthenium argentatum Gray Erigeron argentatus Gray | ||||
| argenteoguttatus argenteoguttata argenteoguttatum | dotted with silver | |||
| argenteus | argente | adj | of silver | |
| o | o | cnct | connective vowel in botanical Latin, usually for Greek words but in some cases, such as color tingeing, for Latin words | |
| gutta | gutt | noun/c | drop of liquid; a little bit | |
| atus | at | adj | adjectival suffix for nouns: possessive of or likeness of something (with, shaped, made)/ for verb participles: a completed action, -ed | |
| argenteomarginatus argenteomarginata argenteomarginatum | silver edged | |||
| argenteus | argente | adj | of silver | |
| o | o | cnct | connective vowel in botanical Latin, usually for Greek words but in some cases, such as color tingeing, for Latin words | |
| marginatus | marginat | ppar | marginato to furnish with a border; to border, enclose with a margin | |
| argenteus argentea argenteum | silvery | |||
| argenteus | argente | adj | of silver | |
| Acaena argentea Ruiz. & Pav. Bryum argenteum Hedw. Lupinus argenteus Pursh | ||||
| argillaceus argillacea argillaceum | clay like | |||
| argilla | argill | noun/f | αργιλλοϛ white clay, potter's clay | |
| aceus | ace | adj | adjective suffix for nouns: resembles, of..., ...like, mixture using, fragrance of; borrowed from nouns declined like ...ax, ...acis + eus | |
| Digitaria argillacea (A.S. Hitchc. & Chase) Fern. | ||||
| argillicola argillicolae | growing in clay | |||
| argilla | argill | noun/f | αργιλλοϛ white clay, potter's clay | |
| i | i | cnct | connective vowel used by botanical Latin | |
| cola | col | noun/f | incola dweller, inhabitant -> -cola/-colus suffix | |
| Oenothera argillicola Mackenzie | ||||
| argophyllus argophylla argophyllum | shining or whitish leaves | |||
| argos | arg | adj | αργοϛ bright, glistening; swift footed (flashing feet); light footed | |
| o | o | cnct | connective vowel in botanical Latin, usually for Greek words but in some cases, such as color tingeing, for Latin words | |
| phyllon | phyll | noun/n | φυλλον leaf; foliage | |
| us | adj | Latinizing suffix | ||
| Olearia argophylla (Labill.) Benth. Eriogonum argophyllum Reveal Aragallus argophyllus Rydb. | ||||
| argutifolius argutifolia argutifolium | sharply toothed leaves | |||
| argutus | argut | adj | that which becomes acute to the senses: sharp, fiery, shrill, clear, pungent, etc | |
| i | i | cnct | connective vowel used by botanical Latin | |
| folius | foli | adj | folium leaf | |
| argutus arguta argutum | sharply toothed | |||
| argutus | argut | adj | that which becomes acute to the senses: sharp, fiery, shrill, clear, pungent, etc | |
| Dryopteris arguta (Kaulfuss) Watt Hieracium argutum Nutt. Rubus argutus Link | ||||
| argyraceus argyracea argyraceum | like silver | |||
| argyros | argyr | noun/m | αργυροϛ silver; money | |
| aceus | ace | adj | adjective suffix for nouns: resembles, of..., ...like, mixture using, fragrance of; borrowed from nouns declined like ...ax, ...acis + eus | |
| Salix argyracea E. L. Wolf | ||||
| argyraeus argyraea argyraeum | silvery | |||
| argyros | argyr | noun/m | αργυροϛ silver; money | |
| aeus | ae | adj | -αεοϛ adjective suffix for nouns: belonging to, from, of | |
| Argythamnia argyraea Cory Hieracium argyraeum Small Lotus argyraeus (Greene) Greene | ||||
| argyreus argyrea argyreum | silvery | |||
| argyreos | argyre | adj | αργυρεοϛ of silver, shining like silver | |
| us | adj | Latinizing suffix | ||
| Draba argyrea Rydb. | ||||
| argyrocomus argyrocoma argyrocomum | silvery hair | |||
| argyros | argyr | noun/m | αργυροϛ silver; money | |
| o | o | cnct | connective vowel in botanical Latin, usually for Greek words but in some cases, such as color tingeing, for Latin words | |
| come | com | noun/f | κομη hair hair, foliage | |
| us | adj | Latinizing suffix | ||
| Paronychia argyrocoma (Michx.) Nutt. | ||||
| argyroneurus argyroneura argyroneurum | silver veined | |||
| argyros | argyr | noun/m | αργυροϛ silver; money | |
| o | o | cnct | connective vowel in botanical Latin, usually for Greek words but in some cases, such as color tingeing, for Latin words | |
| neuron | neur | noun/n | νευρον sinew, tendon, nerve | |
| us | adj | Latinizing suffix | ||
| Aristolochia argyroneura hort. ex Gentil | ||||
| argyrophyllus argyrophylla argyrophyllum | silvery leaves | |||
| argyros | argyr | noun/m | αργυροϛ silver; money | |
| o | o | cnct | connective vowel in botanical Latin, usually for Greek words but in some cases, such as color tingeing, for Latin words | |
| phyllon | phyll | noun/n | φυλλον leaf; foliage | |
| us | adj | Latinizing suffix | ||
| Acacia argyrophylla Hook. Rhododendron argyrophyllum Franch. | ||||
| argyrotrichus argyrotricha argyrotrichum | silvery haired | |||
| argyros | argyr | noun/m | αργυροϛ silver; money | |
| o | o | cnct | connective vowel in botanical Latin, usually for Greek words but in some cases, such as color tingeing, for Latin words | |
| thrix | trich | noun/f | ϑριξ, τριχοϛ hair, wool, bristle | |
| us | adj | Latinizing suffix | ||
| Digitaria argyrotricha (Andersson) Chiov. Panicum argyrotrichum Andersson | ||||
| aridus arida aridum | arid | |||
| aridus | arid | adj | dry, arid, parched | |
| Andrachne arida (Warnock & M.C. Johnston) G.L. Webster Achnatherum aridum (M.E. Jones) Barkworth Penstemon aridus Rydb. | ||||
| arietinus arietina arietinum | ram like (ram's head) | |||
| aries | ariet | noun/m | ram | |
| inus | in | adj | adjective suffix for nouns: belonging to or resembling | |
| Criosanthes arietina (Ait. f.) House Cicer arietinum L. | ||||
| arifolius arifolia arifolium | leaves like Arum | |||
| aros | ar | noun/f | αρον L (also aron & arum) arum, the wake-robin, variously ascribed to Arum dracunculus (Linnaeus) and Arum italicum | |
| i | i | cnct | connective vowel used by botanical Latin | |
| folius | foli | adj | folium leaf | |
| Hexastylis arifolia (Michx.) Small Asarum arifolium Michx. Rumex arifolius All. | ||||
| aristatus aristata aristatum | long bristle like tip | |||
| arista | arist | noun/f | corn silk; ear of corn | |
| atus | at | adj | adjectival suffix for nouns: possessive of or likeness of something (with, shaped, made)/ for verb participles: a completed action, -ed | |
| Gaillardia aristata Pursh Anthoxanthum aristatum Boiss. Psilurus aristatus (L.) Trevis. | ||||
| aristosus aristosa aristosum | bearded | |||
| aristosus | aristos | adj | with many beards like those on the seed heads of grasses | |
| Bidens aristosa (Michx.) Britt. Brachyelytrum aristosum (Michx.) Trel. | ||||
| arizelus arizela arizelum | obvious, noteable | |||
| arizelos | arizel | adj | αριζηλοϛ very plain, clear, distinct, much known as a star, voice, admirable person | |
| us | adj | Latinizing suffix | ||
| Rhododendron arizelum Balf. f. & Forrest | ||||
| armatus armata armatum | armed (spiny) | |||
| armatus | armat | adj | armor | |
| Acacia armata R. Br. Eryngium armatum (S. Wats.) Coult. & Rose Osmanthus armatus Diels | ||||
| armeniacus armeniaca armeniacum | Armenian, apricot colored | |||
| armeniacus | armeniac | adj | Armeniacus Armenian | |
| armeniaca apricot tree | ||||
| Androsace armeniaca Duby Muscari armeniacum Leichtl. ex Baker Rubus armeniacus Focke | ||||
| armenius armenia armenium | Armenian | |||
| armenius | armeni | adj | Armenius of Armenia, Armenian | |
| Trifolium armenium Willd. | ||||
| armerius armeria armerium | L. name for Dianthus | |||
| armerius | armeri | adj | name for Dianthus barbatus | |
| Dianthus armeria L. | ||||
| armiger armigera armigerum | spiny, prickly | |||
| armiger | armiger | adj/ | bearing weapons, arms | |
| armillaris armillaris armillare | collared | |||
| armilla | armill | noun/f | armlet, arm ornament | |
| aris | ar | adj | alis adjective suffix for nouns: of or pertaining to | |
| Melaleuca armillaris (Sol. ex Gaertn.) Sm. | ||||
| armillatus armillata armillatum | collared | |||
| armilla | armill | noun/f | armlet, arm ornament | |
| atus | at | adj | adjectival suffix for nouns: possessive of or likeness of something (with, shaped, made)/ for verb participles: a completed action, -ed | |
| aromaticus aromatica aromaticum | fragrant | |||
| aromaticos | aromatic | adj | αρωματικοϛ aromatic - from aroma (spice) | |
| us | adj | Latinizing suffix | ||
| Rhus aromatica Ait. Cinnamomum aromaticum Nees Campulosus aromaticus (Walt.) Scribn. | ||||
| arrectus arrecta arrectum | upright, erect | |||
| arrectus | arrect | padj | steep, precipitous | |
| Brachiaria arrecta (T. Dur. & Schinz) Stent Glottiphyllum arrectum N. E. Br. Astragalus arrectus Gray |


About Video Cinematic
video_cinematic is an oxwall plugin. Video Cinematic Mode allow your oxwall
video player has ability to dimm the light around and center the video. If you love this plugin please buy me a beer (via paypal to thaolt@songphi.com). Don't hesitate to send me an email on how you want to improve this plugin or ideas of new features, etc.
Developing
git clone https://github.com/ycicom/video_cinematic.git
Making plugin package from source
cd /path/to/video_cinematic
./make-pkg.sh
Contributing
Any contributing is appreciated. I need help in translating into other languages.
Credits
Libraries that included:
Many thanks for authors of these libraries
Licenses of them can be found in /docs/LICENSES/
Changes Log
1.2.4
- bugfix: routing handling causing fatal getDispatchAttr()
1.2.3
- bugfix: display logo on the lightbox video player
- bugfix: correct lightbox (popup player) css
1.2.2
- feature: auto disabled all popup players if PHP version less than 5.3.0
1.2.1
- added settings for admin to enable/disable overlay player on dashboard/profile/listing pages
- minify JS & CSS
- clean-up un-used files
1.2.0
- added feature: watch video directly from video listing page (by opening a fancybox)
- improved user interface
- improved admin interface and more settings
- fixed some minor bugs
1.1.0
- option to switch between presets: lite(thin border, no close button, no logo
mark) and full (thick border with close button and logo mark)
- re-wrote plugin structure (organized static contents)
- added setting allows admins to display logo on video cinematic layer
- allow admins changing border color
1.0.2
- added a close cinematic mode button as requested
1.0.1
- correctly restore z-index css attribute of video div layer when closing
cinematic mode
1.0.0


About me
I am a front-end developer, passionate for Internet Technology. LEGO Geek and Rubik's cube Solver. Excellent experience in Front End Development, Cross browser and Responsive Design.
I am a front-end developer, passionate for Internet Technology. LEGO Geek and Rubik's cube Solver. Excellent experience in Front End Development, Cross browser and Responsive Design.
Hackathon
The video could not be loaded.
My skills are
- Creating website/apps with responsive frameworks;
- Grid system, Material Design, Bootstrap and Foundation;
- HTML5/CSS3;
- Developing website templates;
- Javascript, ES6, jQuery, ZeptoJs and familiarity with AngularJS/IONIC;
- CSS (SASS/LESS), OOCSS, SMACSS and BEM;
- Learning React and PWA(Progressive Web Apps);
- High aptitude for diving in and picking up new things;
Contact me


ShopOne - Furniture Store HTML Template
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Select a demo page
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Type in the boxes then click submit to activate your Spark Core or Photon.
Device Core ID:
Get this from the core area when logged in
Access Token :
Get this from the settings area when logged into http://spark.io
Click this first
Enter the value of your Spark Cores IP Address here: Port = 80
Then click the buttons or mouse over the buttons and watch th D7 LED on your core
For simple weblinks to work you would have to change the following code to use the IP determined by clicking the button above D7-ON.....D7-OFF
Some better Javascript buttons demonstrated below.
Easiest
Better
.....
Enter a delay milliseconds per command
Timer result Watch that 10 flashes actually occur.
Lower the ms until only a few flashes occur or the LED does not turn off!
On my network I consistently can get 60 ms per command.


Home
Algorithms
Graphical Demonstration of Disk Scheduling Algorithms
Control panel
Animation controls
Start
Pause
Continue
Reset
Start paused
Animation progress slider
Steps:
Algorithm(s)
FCFS
SSTF
SCAN
C-SCAN
LOOK
C-LOOK
x
Error! Algorithm selection is required!
Track size (min. 10)
Starting track (between 0 and [track size - 1])
Spin direction
From left to right
From right to left
Seek position queue generation (optional)
Unsorted
Sorted
Reverse-sorted
Back and forth
Count
Generate (New)
Generate (Append)
Seek position queue
x
Error! Invalid format or values...
Input field must be filled with whole, positive numbers separated by commas
Numbers cannot be greater than track size
Disk scheduling animation canvas
Download PDF
ALGORITHM
TRACK SIZE
STARTING TRACK
SEEK QUEUE