semPLS:

Structural

Equation

Modeling

Using

Partial

Least

Squares

This

introduction

to

the

R

package

semPLS

is

a

(slightly)

modified

version

of

Monecke

and

Leisch

published

in

the

Journal

of

Statistical

Software.Structural

equation

models

(SEM)

are

very

popular

in

many

disciplines.

The

partial

least

squares

(PLS)

approach

to

SEM

offers

an

alternative

to

covariance

based

SEM,

which

is

especially

suited

for

situations

when

data

is

not

normally

distributed.

PLS

path

modelling

is

referred

to

as

soft-modeling-technique

with

minimum

demands

regarding

measurement

scales,

sample

sizes

and

residual

distributions.

The

semPLS

package

provides

the

capability

to

estimate

PLS

path

models

within

the

R

programming

environment.

Different

setups

for

the

estimation

of

factor

scores

can

be

used.

Furthermore

it

contains

modular

methods

for

computation

of

bootstrap

confidence

intervals,

model

parameters

and

several

quality

indices.

Various

plot

functions

help

to

evaluate

the

model.

The

well

known

mobile

phone

dataset

from

marketing

research

is

used

to

demonstrate

the

features

of

the

package.

Within

the

academic

literature

of

many

fields,

Rigdon

remarks,

structural

equation

modeling

(SEM)

has

taken

up

a

prominent

role.

Whenever

researchers

deal

with

relations

between

constructs

such

as

satisfaction,

role

ambiguity,

or

attitude,

SEM

is

likely

to

be

the

methodology

of

choice.

Since

SEM

is

designed

for

working

with

multiple

related

equations

simultaneously,

it

offers

a

number

of

advantages

over

some

more

familiar

methods

and

therefore

provides

a

general

framework

for

linear

modeling.

SEM

allows

great

flexibility

on

how

the

equations

are

specified.

The

development

of

an

evocative

graphical

language

(McArdle

McArdle

and

McDonald

has

accompanied

the

development

of

SEM

as

a

statistical

method.

Due

to

this

language,

complex

relationships

can

be

presented

in

a

convenient

and

powerful

way

to

others

not

familiar

with

SEM..

The

partial

least

squares

approach

to

SEM

(or

PLS

path

modeling),

originally

developed

by

Wold

Wold

Wold

and

Lohmöller

offers

an

alternative

to

the

more

prominent

covariance-based

(CBSEM,

Jöreskog

Whereas

CBSEM

estimates

model

parameters

so

that

the

discrepancy

between

the

estimated

and

sample

covariance

matrices

is

minimized,

in

PLS

path

models

the

explained

variance

of

the

endogenous

latent

variables

is

maximized

by

estimating

partial

model

relationships

in

an

iterative

sequence

of

ordinary

least

squares

(OLS)

regressions

(e.g.,

Hair,

Ringle,

and

Sarstedt

It

is

worth

mentioning

that

in

PLS

path

modeling

latent

variable

(LV)

scores

are

estimated

as

exact

linear

combinations

of

their

associated

manifest

variables

(MVs)

and

treats

them

as

error

free

substitutes

for

the

manifest

variables.

Whereas

CBSEM

requires

hard

distributional

assumptions,

PLS

path

modeling

is

a

soft-modeling-technique

with

less

rigid

distributional

assumptions

on

the

data.

At

this

point

it

should

be

mentioned,

that

PLS

path

modeling

is

not

to

be

confused

with

PLS

regression.

According

to

Chin

it

can

be

argued,

that

depending

on

the

researcher's

objectives

and

epimistic

view

of

data

to

theory,

properties

of

the

data

at

hand

or

level

of

theoretical

knowledge

and

measurement

development,

PLS

path

modeling

is

more

suitable.

Additionally,

great

interest

in

applying

PLS

path

models

has

been

stimulated

by

the

increasing

need

in

modeling

so

called

formative

constructs,

especially

in

marketing

and

management/organizational

research

(e.g.,

Diamantopoulos

and

Winklhofer

Jarvis,

MacKenzie,

and

Podsakoff

MacKenzie,

Podsakoff,

and

Jarvis

The

application

of

PLS

path

models

in

marketing

is

discussed

in

depth

by

Henseler,

Ringle,

and

Sinkovics

and

Hair,

Sarstedt,

Ringle,

and

Mena

For

a

related

discussion

in

the

field

of

management

information

systems,

see

Ringle,

Sarstedt,

and

Straub

The

semPLS

is

a

package

for

structural

equation

modeling

(SEM)

with

partial

least

squares

(PLS)

in

R

(R

Development

Core

Team

It

is

available

from

the

Comprehensive

R

Archive

Network

at

One

of

the

major

design

goals

is

to

provide

a

comprehensive

open-source

reference

implementation.

The

package

offers.

modular

methods

for

model

fitting,

calculation

of

quality

indices,

etc.,.

plotting

features

for

better

understanding

of

the

multivariate

model

data,.

a

convenient

user

interface

for

specifying,

manipulating,

importing

and

exporting

model

specifications,.

and

an

easily

extensible

infrastructure..

Within

the

package

there

are

two

central

methods.

The

first

is

plsm

which

is

used

to

create

valid

model

specifications.

The

second

is

sempls

which

fits

the

model,

specified

with

plsm.

Factor

scores

can

be

estimated

by

using

three

different

weighting

schemes:

centroid,

factorial

and

path

weighting.

For

the

calculation

of

the

outer

weights,

correlations

can

be

calculated

by

using

Pearson-correlations

for

continuous

data

or

Spearman-or

Kendall-correlations

when

the

scale

of

the

data

has

rather

ordinal

character.

If

the

data

contains

missing

values

it

is

possible

to

use

pairwise

correlations

to

compute

outer

weights.

In

addition

to

the

estimated

factor

scores

and

outer

weights,

sempls

computes

loadings,

path

coefficients

and

total

effects,

as

those

are

the

parameters

of

interest.

For

the

outer

loadings/weights

and

path

coefficients

different

types

of

bootstrap

confidence

intervals

and

standard

errors

are

available.

Calculation

of

quality

indices

(R

Q

Dillon-Goldstein's

ρ,

etc.)

is

done

via

specific

methods.

PLS

path

models

specified

with

plsm

can

be

easily

manipulated

by

a

variety

of

utility

methods.

Models

specified

in

SmartPLS

can

be

imported.

Several

plot

types

(e.g.

pairs

plots

of

MV

blocks,

convergence

diagnostic

of

outer

weights,

kernel

density

estimates

of

residuals/bootstrap

parameters,

parallel

coordinates

of

bootstrap

parameters,

etc.)

support

the

researcher

in

evaluating

their

models.

Finally

a

graphical

representation

of

the

model

including

outer

loadings

and

path

coefficients

can

be

written

to

a

DOT

file

which

can

be

rendered

and

plotted

by

dot

(Gansner,

Koutsofios,

and

North

a

layout

program

contained

in

O O O O O B I I O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O B B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Bayesian Estimation of Multi-Trap RTN Parameters Using Markov Chain Monte Carlo Method Random telegraph noise (RTN) is a phenomenon that is considered to limit the reliability and performance of circuits using advanced devices. The time constants of carrier capture and emission and the associated change in the threshold voltage are important parameters commonly included in various models, but their extraction from time-domain observations has been a difficult task. In this study, we propose a statistical method for simultaneously estimating interrelated parameters: the time constants and magnitude of the threshold voltage shift. Our method is based on a graphical network representation, and the parameters are estimated using the Markov chain Monte Carlo method. Experimental application of the proposed method to synthetic and measured time-domain RTN signals was successful. The proposed method can handle interrelated parameters of multiple traps and thereby contributes to the construction of more accurate RTN models. Random telegraph noise (RTN) is the temporal change in threshold voltages observed in metal-oxide-semiconductor (MOS) transistors. Figure 1 shows the RTN generation mechanism. The capture of a carrier to an electrically active trap (defect) and its release cause a charge change in the dielectric, leading to shifts in the threshold voltage [1] . As device sizes shrink, the effects of RTN are expected to become more serious [2] . Nagumo et al. introduced a time lag plot (TLP) to investigate the magnitude of the threshold voltage shift caused by RTN [3] . They further analyzed other parameters such as trap position by measuring devices with a single observable trap [4] . Miki et al. [5] and Realov et al. [6] applied a hidden Markov model (HMM) for investigating the time constants of RTN. In the conventional studies, however, extraction of amplitudes has been limited to devices that have equal to or less than two traps. Since devices having more than two traps in measurement data are common, it is important to develop a method that is applicable to arbitrary number of traps. Furthermore, the extraction of time constants cannot be conducted through HMM. Hence, a sep- arate process, such as application of TLP, is required. Application of two different analyses to extract interrelated parameters leads to insufficient accuracy in parameter extraction. In Table 1 , characteristics of the proposed and the existing methods are compared. Figure 2 shows two examples of measured threshold voltage shifts due to RTN. In the case of devices with a single trap, we can estimate the magnitude of the threshold voltage shift simply by calculating the distance between two peaks in threshold voltage histogram. On the other hand, in the case of multiple traps, parameter extraction becomes much more complicated because parameters such as magnitudes, time constants, and trap states are strongly interrelated. Furthermore, it is an under-determined problem because it requires reconstruction of multi-trap time series sequences from only the threshold voltage fluctuation, which is superimposed with multi-trap activity. Hence, the direct use of HMM, as in conventional estimation methods, is inappropriate. In this paper, we propose a novel method for extracting RTN parameters based on a machine learning method. We construct a statistical generation model that reflects the device physics of RTN, in which magnitudes, time constants, and trap states are random parameter variables. In our method, these parameters are estimated using Markov chain Monte Carlo methods (MCMC) so that they best fit the model to an observed RTN signal.. The proposed method has advantages over existing methods, which are summarized as follows.. 1. There is no limit to the number of traps. More than two traps can be treated by the proposed method. 2. All RTN parameters are directly estimated. No postprocessing is necessary.. Copyright c 2012 The Institute of Electronics, Information and Communication Engineers Table 1 Summary of capabilities among the proposed and the existing methods. In this study, we have proposed a statistical machine learning approach to simultaneously estimate the amplitude and time constants of each trap from measured threshold voltage fluctuation caused by RTN. The proposed method can handle interrelated parameters of mutiple traps and thereby contributes to the construction of more accurate RTN models. The experiments using synthetic and measurement data showed that the proposed method successfully estimated the magnitudes of the threshold voltage shift. Further improvement of the estimation algorithm and application of the proposed method to a variety of MOS devices are the subject of future work.

B I O O O O O B I I I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Feature engineering for MEDLINE citation categorization with MeSH Background: Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations. Results: Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance. Conclusions: We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system. Research in MeSH indexing has mostly used the bag-ofwords representation, leaving other more sophisticated features aside. In this paper, we have studied the feasibility of exploiting the syntactic, semantic and structural properties of texts in order to improve classification of MEDLINE citations with different MeSH headings. Our main conclusion is that, even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation for MeSH indexing. The combination allows improving the performance over the MTI system, that has been shown to be a hard baseline to improve on [28] . We have also found that the selection of the learning algorithm has an influence in the overall performance of the system. Algorithms that are more resilient to the imbalance of the data set show improved performance.. As future work, we plan to take further features into consideration, including the part-of-speech, using automatic summaries from PMC full text articles instead of the abstracts for extracting the classification features, exploiting more in depth the argumentative structure of the abstract, for instance, by including as features only the tokens from relevant sections, and exploring additional features from MEDLINE or full text articles. We have not investigated the combination of learning algorithms in this work, which has previously shown to improve performance [56] . We did not investigate the selection of MeSH headings according to the best performing combination of features and methods [11] . Recent work using deep learning for MeSH indexing [58] shows promising results and could be considered to obtain a better set of features automatically. All this could be explored as future work. Furthermore, since overfitting is a problem with some learning algorithms and feature selection seems to decrease performance [29] , larger citation sets could be considered in further experiments. The experiments show that unigrams and bigrams are a strong baseline compared to other more sophisticated features, which is in agreement with previous work. A conceptual representation based on MetaMap shows similar performance compared to unigrams. In addition, combining the features improves the classification performance. When combining all feature sets, even better results than the MTI system are achieved.. The machine learning algorithm has an influence as well on the performance. Looking at the overall results, SVM-perf has the best average performance for the different feature sets (even better the AdaBoostM1 with oversampling). AdaBoostM1 with oversampling performs better than AdaBoostM1 using the natural class distribution. This means that learning algorithms prepared to deal with data set imbalance perform better. We find as well that when all the features are used, AdaBoostM1 with oversampling has decreased performance, which might be due to over-fitting of the learned model. Previous work on feature selection for MeSH categorization has shown a decrease in performance when feature selection is used [29] , and it has been seen as well for algorithms like SVM in more general domains [57] . A possible solution might be to increase the size of the training set when a large number of features is being used. Figure 1 shows the F-measure per MeSH heading of the best combination of features (TIAB+bigram+all) and the best performance algorithm (SVM-perf ). Our results corroborate previous work that has shown that the performance decreases for the less frequent MeSH headings compared to more frequent ones [11] . We can see this with the most frequent MeSH headings: Humans, Male and Female. Except for a couple of MeSH headings (Animals and Mice), the performance of the algorithms with respect to these MeSH headings decreases. However, we have observed that using learning algorithms resilient to class imbalance shows improved performance that has a positive impact on less frequent ones. The most frequently used feature model is the so called bag-of-words: each position in the input feature vector corresponds to a given word or phrase and stores either a binary value indicating its presence or a numerical value indicating its frequency or even its TF-IDF [12, 13] . Other authors have used phrases rather than individual words [14, 15] , but the results obtained are not significantly better (or are even worse) than those of the bag-ofwords approach. Representations based on named entities have also been explored. They have proved to be useful when a reduction of the number of features is needed [3] , but do not add any classification power.. There is previous work on feature engineering for text categorization not bound to the biomedical domain [3] that examines a large set of text representations, including lexical (e.g., bag-of-words), syntactic (e.g., noun phrases and key phrases) and semantic features (e.g., synonym and hypernym relations and concepts from WordNet). Syntactic features have also been used in combination with other lexical or semantic ones.. Previous work has attempted to improve classification by using bigrams as features [16] . While using bigrams together with unigrams has proved to be potentially beneficial for text classification, using bigrams alone leads in most cases to a decrease in results comparing with the use of bags-of-words.. Lewis [17] was the first to study the effect of syntactic phrases in text categorization, and found that a Naïve Bayesian classifier with only noun phrases yields significantly lower results than a standard classifier based on bag-of-words.. In [18] , part-of-speech tags are appended to words in the feature vector, and the position of each word in the text (whether it appeared in the first quarter, last quarter, or middle half of the document) is also codified. None of these features, however, improves significantly from using unigrams alone. Furnkranz et al. [19] used automatically extracted linguistic patterns as features to a Naïve Bayesian classifier, and got a consistent improvement in precision. More recent efforts have proposed the use of external resources in order to semantically enrich the feature sets. In [20] , for instance, common-sense and domain-specific knowledge are used to enrich the bag-ofwords, using repositories of knowledge such as the Open Directory Project (ODP), Yahoo! Web Directory, and the Wikipedia encyclopedia. Text categorization is the task of automatically assigning pre-defined labels to text [1] . Even though several methods can be used, machine learning is appealing due to the large data sets that are available as training data that allow for automating the development of categorization models effectively.. In the biomedical domain, research in automatic text classification is usually conducted in the context of indexing MEDLINE ® citations with MeSH ® descriptors. There are over 23 M citations in MEDLINE, with a growth rate of over 800 k new citations every year. This growth rate makes it difficult to keep up-to-date with new discoveries. To help cataloguing and searching biomedical documents, the US National Library of Medicine (NLM) ® has produced the medical subject headings (MeSH) controlled vocabulary. Each MEDLINE citation is manually assigned a number of relevant medical subject headings that classify the document according to its topic. Manual indexing is, however, costly. As stated in [2] , MEDLINE indexing is the responsibility of a relatively small group of highly qualified indexing contractors and staff at the NLM who find it difficult to maintain the quality of this huge resource. In this situation, automatic methods to categorize citations might be relevant.. Current work on automatic biomedical text categorization based on machine learning has been dealing largely with bag-of-words representations. Bag-of-words based on unigrams are easy and fast to build and have shown good performance on many text categorization tasks [3] . However, specialized domains such as the biomedical one suffer from a varied terminology which might be too sparse to efficiently train machine learning models. For instance, when building a categorization rule for prostate cancer documents, there are terms with low frequency that are highly relevant for classification, e.g. prostate adenocarcinoma. Ontologies and terminological resources like the Unified Medical Language System (UMLS ® ) [4] can help in the normalization of terms found in text and could be used to derive additional feature sets (e.g., through synonymy and generalization). This normalization has already been proposed for indexing MEDLINE with MeSH controlled vocabulary in the Restrict-to-MESH algorithm [5] .. There is also information latent in the structure of the text that is not usually considered in the categorization task, i.e., if the terms come from a specific section of a citation. For instance, if prostate cancer is mentioned only in the "Background" section, then it is not as relevant as if it is found in the title of the article. This information could be used by the classifier in order to build a better categorization model, as has already been shown useful for the retrieval [6] and the selection of topic relevant sentences [7] [8] [9] .. In addition, there is meta-data that is usually linked to biomedical citations that is not taken into account and could be used to provide a better categorization performance. For instance, there are specialized journals in which certain types of articles are published, e.g. specialized journals for breast cancer like Breast Cancer Research.. Other issues with the features used in text classification are not considered in this work. For instance, there are learning algorithms that benefit from feature selection or better tuning of weights used to represent the features [10] . In previous work, several machine learning (ML) approaches and their combination [11] have been evaluated within the biomedical domain, trying to reproduce, for instance, MeSH indexing. On the other hand, most previous work relies on comparing or combining several ML algorithms (Bayesian models, neural networks, decision trees, regression, etc.) on bag-of-words representations. Little attention has been given to measuring the impact of feature engineering.. The motivation of this work is to evaluate the performance of machine learning algorithms on several types of features in the context of text categorization (in particular, categorization of MEDLINE citations with different MeSH headings), testing representations based on lexical, syntactic, semantic and argumentative information. In addition to evaluating each individual feature set, we test different combinations of features. We also evaluate several machine learning approaches to overcome the imbalance between the small number of positives versus the large number of negatives.. Overall, we find that the bag-of-words representation is a competitive baseline, slightly improved by the use of higher order n-grams. We also find that the combination of different features outperforms the bag-of-word baseline. In addition, a large improvement is obtained by using techniques that target the imbalance in the training set. Finally, we observe that the performance may vary depending on the evaluated MeSH heading, which might imply that optimal performance would require selecting a specific feature set in some particular cases.

B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Using the minimum description length principle to reduce the rate of false positives of best-fit algorithms The inference of gene regulatory networks is a core problem in systems biology. Many inference algorithms have been proposed and all suffer from false positives. In this paper, we use the minimum description length (MDL) principle to reduce the rate of false positives for best-fit algorithms. The performance of these algorithms is evaluated via two metrics: the normalized-edge Hamming distance and the steady-state distribution distance. Results for synthetic networks and a well-studied budding-yeast cell cycle network show that MDL-based filtering is more effective than filtering based on conditional mutual information (CMI). In addition, MDL-based filtering provides better inference than the MDL algorithm itself. A key goal in systems biology is to characterize the molecular mechanisms that govern specific cellular behavior and processes. Models of gene regulatory networks run the gamut from coarse-grained discrete networks to detailed descriptions of such networks by stochastic differential equations [1] . Boolean networks and the more general class of probabilistic Boolean networks are among the most popular approaches for modeling gene networks because they provide a structured way to study biological phenomena (e.g., the cell cycle) and diseases (e.g., cancer), ultimately leading to systems-based therapeutic strategies. The inference of gene networks from high-throughput genomic data is an ill-posed problem known as reverse engineering. It is particularly challenging when dealing with small sample sizes because the number of variables in the system (e.g., the number of genes) typically is much greater than the number of observations [2] . Many inference algorithms have been proposed to elucidate the regulatory relationships between genes, such as Reveal [3] , ARACNE [4] , the minimum description length principle (MDL) [5] [6] [7] [8] [9] , the coefficient of determination (CoD) [10, 11] , and the best-fit extension [12, 13] .. False positives are a common problem in inference, especially when dealing with small sample sizes and noisy conditions. In fact, false positives are a kind of structural redundancy. Given three genes, x 1 , x 2 , and x 3 , they may interact in a chain-like manner, such as x 1 → x 2 → x 3 or x 1 ← x 2 ← x 3 ; or in a hub-based way, such as x 1 → x 2 ← x 3 or x 1 ← x 2 → x 3 . Indirect interactions between two genes may produce some correlation in their expression data, which can lead to a false regulation detection by inference algorithms. The data-processing inequality (DPI) was first used in ARACNE, which aims to reduce the false positives produced by chain interaction [4] . Later, conditional mutual information (CMI) was proposed to tackle the false positives produced by both the chain-like and hub-based interactions [14] . Because the conditioning gene, x 2 , is usually not known, a greedy search strategy was adopted to check if the CMI between x 1 and x 3 conditioned on some other genes was below a given threshold. To check the CMI on other unrelated genes is problematic. Not only is it computationally burdensome, it also suffers from an enormous multiplecomparisons problem. Moreover, since the interaction strength between genes generally varies a lot, their being both strong and weak interactions, how to set an appropriate threshold is a key problem.. A recent study shows that the best-fit algorithm appears to give the best results for recovering regulatory relationships in comparison to the aforementioned algorithms [15] . In the present paper, we propose to reduce the false positives of the best-fit algorithm by using the MDL principle. Simulation results show that it is more effective than the CMI-based method and can reduce the false positives in the MDL algorithm in [5] . In effect, the false-positive reducing procedure acts as a filter for removing false positives.. The aim of filtering in the present framework is to reduce the number of false positive connections. As with any false-positive reducing algorithm, this will invariably increase the number of false negatives, meaning more missing connections. Thus, two questions must be addressed. First, what benefits accrue from reducing the number of false positives? Second, does the increase in false negatives significantly impact inference performance?. A salient problem in translational genomics is the utilization of gene regulatory networks in determining therapeutic intervention strategies [2, 16, 17] . A big obstacle in deriving optimal treatment strategies from networks is the computational complexity arising directly from network complexity. Hence, significant effort has been focused on network reduction [18, 19] . As with any compression scheme, reduction methods sacrifice information in return for computational tractability. Because genes are removed from the network based upon their regulatory relations with other genes, false positives are particularly troublesome. First, they increase the amount of reduction necessary and second, they compete with true positive connections for retention in the reduced network. While it is true that an increase in false negatives is not beneficial, a missing connection creates no additional computational burden (in fact, reduces computation) and plays no role in the reduction procedure. Now, for the caveat, all of this is fine, so long as the accuracy of the original inference algorithm is not adversely impacted. Practically, this means that, relative to some distance function between a ground-truth network and an inferred network (which quantifies inference accuracy), the distance is not increased when using the modified false-positive reducing algorithm in place of the original algorithm. In this paper, we will consider two distance functions, one based on the hamming distance between the ground-truth and inferred networks and the other based on the difference between the steady-state distributions of the ground-truth and inferred networks. This paper is organized as follows: Background information and necessary definitions are given in Section 2. The implementation of MDL, the best-fit algorithm, and CMI-and MDL-based filtering is then introduced in Reducing the rate of false positives is an important issue in network inference. In this paper, we address this question by using the minimum description length (MDL) principle. Specifically, we apply the MDL measurement technique proposed by Zhao et al. to filter the model obtained by two best-fit algorithms (best-fit-I and best-fit-II). We compare the performance of MDL, best-fit-I, and best-fit-II filtered by CMI and MDL both on simulated networks and on an artificial model of budding yeast. The results show that, as determined by the distance metrics μ e ham and μ ssd , MDL-based filtering does not degrade inference performance, can improve inference performance, and is more effective than CMIbased filtering. Moreover, the combination of MDL filtering with the best-fit algorithm can even outperform the MDL algorithm alone. Additionally, applying MDLbased filtering is computationally less burdensome than using the MDL algorithm alone because calculating the data-coding length L D is more complex than calculating the error estimate of the best-fit algorithm, and the complexity of the calculation increases dramatically as the sample size m increases. Last but not the least, MDL-based filtering can also be applied to the results of other minimal error algorithms such as CoD. Based on the common assumption that genetic regulatory networks are sparsely connected, we restrict simulated Boolean networks to a scale-free topology with maximal connectivity K = 4 and average connectivity k = 2. The best-fit algorithm searches for the best-fit function for each gene by exhaustively searching for all combinations of potential regulator sets. The search space grows exponentially with the number of genes. In practice, the limit k i ≤ 3 is generally applied to mitigate model complexity. In this paper, we restrict best-fit-algorithm searches to combinations of 1, 2, or 3 possible regulators. The combinatorial set with the smallest error is then selected as the regulatory set. We call this best-fit-I. In practice, the minimal error predictor set may not unique. We employ the heuristic that each of them can be viewed as fitting the target gene in a different way and if one gene occurs frequently in those sets, then it is highly likely to be a true regulatory gene. Thus, we can determine the regulatory set by applying the majority rule in these sets. Here, we refer to this algorithm as best-fit-II.. Then CMI and MDL criteria are used to filter falsepositive connections. For each regulatory connection, if the CMI for one of the remaining genes is less than 0.005, then the gene is deleted; otherwise, it remains. The MDL criterion is applied to each target gene x i . Given its parent set, Pa(x i ), we delete the regulatory gene x j ∈ Pa(x i ) that can maximally reduce its coding length L i for each point in time, repeating this process until the deletion of one regulatory gene causes L i to increase. We implement an MDL inference algorithm by directly searching the combination of 1, 2, or 3 possible regulators with minimal coding length L i . The free parameter τ in Equation 6 is set to 0.2.. We have analyzed CMI-and MDL-based filtering by using both synthetic networks as well as the well-studied cell-cycle model known as the budding-yeast network. We compare them with the ground-truth network according to the following two distances [15, 23] :. (1) The normalized-edge Hamming distance:. where FN and FP represent the number of false-negative and false-positive wires, respectively, and P represents the total number of positive wires. This Hamming distance reflects the accuracy of the recovered regulatory relationships.. (2) The steady-state distribution distance:. where π k and π ′ k are the steady-state probabilities state x k in the ground-truth and inferred network, respectively. The steady-state distribution distance reflects the degree to which an inferred network approximates the long-run behavior of the ground-truth network.

O O B I I I O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


New Techniques for Anonymous HIBE with Short Ciphertexts in Prime Order Groups Anonymous hierarchical identity based encryption (HIBE) is an extension of identity based encryption (IBE) that can use an arbitrary string like an e-mail address for a public key, and it additionally provide the anonymity of identity in ciphertexts. Using the anonymous HIBE schemes, it is possible to construct anonymous communication systems and public key encryption with keyword search. This paper presents an anonymous HIBE scheme with constant size ciphertexts under prime order symmetric bilinear groups, and shows that it is secure under the selective security model. Previous anonymous HIBE schemes were constructed to have linear size ciphertexts, to use composite order bilinear groups, or to use asymmetric bilinear groups that is a special type of bilinear groups. Our construction is the first efficient anonymous HIBE scheme that has constant size ciphertexts and that uses prime order symmetric bilinear groups. Compared to the previous scheme of composite order bilinear groups, ours is ten times faster. To achieve our construction, we first devise a novel cancelable random blinding technique. The random blinding property of our technique provides the anonymity of our construction, and the cancellation property of our technique enables decryption. A public key encryption system is one of the essential components of efficient and secure digital communication systems. Identity based encryption (IBE) is public key encryption where an arbitrary identity string like an e-mail address can be used as a public key. Therefore, IBE is a new paradigm of public key encryption that can solve the public key distribution and management problems in public key encryption. Hierarchical IBE (HIBE) is a generalization of IBE such that the identity is represented as a hierarchical structure and a private key can be delegated from a higher level user to a lower level user. The concept of IBE was suggested by Shamir in 1984. However, the first efficient and secure construction of IBE was proposed by Boneh and Franklin using bilinear groups in [7, 8] . The construction of HIBE was presented by Gentry and Silverberg in [21] . After that, other constructions of IBE and HIBE were presented in [3, 5, 30, 19, 31, 24] .. The security notion of IBE and HIBE is defined as indistinguishability of messages. That is, the ciphertext of IBE and HIBE provides a message hiding property (semantic security). This property of security is were proposed in [12, 29, 27, 25, 16] . The previous strategies of designing anonymous HIBE schemes are classified into four methods.. The first method is a linear splitting technique that was devised by Boyen and Waters [12] . This method divides the random exponent of a ciphertext as two different random values. In Boneh and Boyen's IBE [3] , an adversary can easily break the anonymity since it can create a bilinear pairing equation like e(g t , (hu ID )) = e(g, (hu ID ) t ) where g t and (hu ID ) t are ciphertext components with a random t. The linear splitting technique prevents the creation of bilinear pairing equation by splitting the random t to t 1 ,t 2 where t = t 1 + t 2 . Intuitively speaking, this technique represents a ciphertext as a random point on a 2-dimensional plane using two random scalars t 1 ,t 2 . The anonymity of ciphertexts is easily obtained because a distinguishing problem whether a random point is on a 2-dimensional plane or 3-dimensional space is equivalent to the decisional Linear (DLIN) assumption. Though, this technique enables the construction of anonymous IBE, it is not sufficient for the construction of anonymous HIBE. To achieve anonymous HIBE, Boyen and Waters additionally devised a private re-randomization technique. In this technique, additional re-randomization components are included in a private key instead of a public key, and the re-randomization components can not be used to attack the anonymity by making the re-randomization process to be private. Boyen and Waters constructed the first anonymous HIBE scheme with linear size ciphertexts under prime order symmetric bilinear groups using the two techniques, and proved its security under the decisional Bilinear Diffie-Hellman and DLIN assumptions.. The second method is to use composite order bilinear groups. A composite order bilinear group consists of prime order bilinear subgroups where each subgroup is orthogonal to other subgroups. In the construction of ciphertexts, we use one subgroup G p 1 to implement a scheme and use another subgroup G p 2 to randomize the ciphertext (random blinding). In the construction of private keys, we use G p 1 only since it is orthogonal to G p 2 . The random blinding elements in ciphertexts provide the anonymity of ciphertexts, and the orthogonal property of subgroups enables the cancellation of random blinding in decryption process. Shi and Waters constructed a delegatable hidden vector encryption (dHVE) scheme under composite order bilinear groups, and they showed that it imply an anonymous HIBE scheme [29] . Seo et al. constructed an anonymous HIBE scheme with constant size ciphertexts under composite order bilinear groups [27] . However, the disadvantage of composite order bilinear groups is that the group order n should be larger than 1024 bits to defeat the integer factorization attacks. Therefore, using composite order bilinear groups is inefficient from point of view of ciphertext size and pairing operations when it is compared to prime order bilinear groups since prime order bilinear groups only requires 160 bits size of group order.. The third method is to use asymmetric bilinear groups. The asymmetric bilinear group is a prime order bilinear groups with an asymmetric bilinear map e : G 1 × G 2 → G T where G 1 , G 2 are different and there are no efficiently computable homomorphisms between them. In asymmetric bilinear groups, the decision Diffie-Hellman (DDH) assumption holds in two groups G 1 and G 2 . Thus, the previous IBE schemes that do not provide the anonymity are easily converted to anonymous IBE schemes on asymmetric bilinear groups. If we apply the private re-randomization techniques of Boyen and Waters to previous HIBE schemes that are not anonymous, then anonymous HIBE schemes are easily obtained [12, 16] . Additionally, anonymous HIBE schemes under composite order bilinear groups are also converted to anonymous HIBE schemes under asymmetric prime order bilinear groups [16, 18] . However, asymmetric bilinear groups have disadvantages such that it is a special kind of prime order bilinear groups and it requires strong assumptions for the proof of security.. The fourth method is to use dual pairing vector space that was devised by Okamoto and Takashima in [25] . The dual pairing vector space is higher dimensional vector space of bilinear groups with two important properties, namely, the hardness of decomposability and the existence of dual orthogonal basis. The hard- ness of decomposability says that is is hard to decompose basis vectors from the ciphertext vector, and this property provides the anonymity of ciphertexts. The existence of dual orthogonal basis says that it is possible to compute inner product of a ciphertext vector and a private key vector, and this property enables the decryption of ciphertexts. Okamoto and Takashima constructed a hierarchical predicate encryption (HPE) scheme using the dual pairing vector space, and showed that 2l + 3 dimensional HPE imply l-level anonymous HIBE [25] . However, the disadvantage of this approach is that it is hard to construct an anonymous HIBE scheme with constant size ciphertexts. In this paper, we presented a new cancelable random blinding technique for the construction of anonymous HIBE, and this technique is different from the previous known techniques. Using our technique, we constructed an anonymous HIBE scheme with constant size ciphertexts under prime order symmetric bilinear groups, and proved its selective model security. Our technique has an independent interest, and it may be possible to use this technique for the construction of other encryption schemes in prime order bilinear groups.. An interesting open problem is to construct an anonymous HIBE scheme with constant size ciphertexts under prime order symmetric bilinear groups that can be prove to be fully secure with reasonable loss of reduction. One idea for this construction is to use the dual system encryption method by Waters [31, 24] . However, the simple combination of these methods does not solve the problem because the dual encryption system of [31, 24] does not work for an HIBE scheme with constant size ciphertexts under prime order symmetric bilinear groups. Boneh and Franklin constructed the first efficient and secure IBE scheme using bilinear groups and proved its security under the random oracle model [7, 8] . Boneh and Boyen constructed two efficient IBE schemes without random oracles and proved that they are secure under a weaker selective-ID security model [3] . Waters proposed a fully secure IBE scheme without random oracles [30] , and Gentry proposed a fully secure IBE scheme with tight security reduction using a strong assumption [19] . The IBE schemes of Boneh and Franklin, and Gentry provide the anonymity of ciphertexts. IBE is not only a new paradigm of public key encryption but also a new solution that provides new methodologies for public key encryption research. That is, IBE can be used to construct public key signature [9, 4, 30, 31] , chosen ciphertext secure public key encryption [14] , and public key encryption with keyword search [6, 1] .. IBE can be extended to hierarchical IBE (HIBE) [21, 3, 5, 12, 11, 20, 27, 31, 24] , attribute based encryption (ABE) [26, 22] , and predicate encryption (PE) [6, 1, 10, 29, 23, 25] depends on the structure of identity.. In HIBE, the identities of users are represented as a hierarchical structure, and the private key of a higher level user can be delegated to a lower level user. The concept of HIBE was introduced by Horwitz and Lynn, but the first efficient and secure construction was proposed by Gentry and Silverberg [21] . Boneh and Boyen constructed an efficient HIBE scheme without random oracles [3] , and Boneh et al. constructed an efficient HIBE scheme with constant size ciphertexts [5] . Boyen and Waters constructed the first anonymous HIBE scheme [12] . In contrast to IBE, it is hard to prove the security of HIBE under full model security with efficient reduction because of its hierarchical structure of identity. Recently, Gentry and Halevi constructed fully secure HIBE scheme using a strong assumption [20] , and Waters also constructed fully secure HIBE scheme by introducing dual system encryption [31, 24] . The main applications of HIBE are forward secure public key encryption [13] and public key broadcast encryption [15] .. In ABE, a private key is associated with an access structure A and a ciphertext is associated with a set S of attributes. If S ⊆ A, then the user of a private key A can decrypt the ciphertext of S. The concept of ABE was introduced by Sahai and Waters, and they proposed a fuzzy IBE that is a special kind of ABE [26] . Goyal et al. constructed an ABE scheme that supports general access structures [22] . If an ABE scheme supports the delegation capability of private keys, then it can be converted to an HIBE scheme [22] . However, there is no ABE scheme that support the anonymity of attributes in contrast to HIBE schemes.. In PE, a ciphertext is associated with a vector x and a private key is associated with a predicate f where the ciphertext provides the anonymity of the vector x. If f (x) = 1, then the user of a private key f can decrypt the ciphertext, but the ciphertext gives no information except f (x) = 1. The concept of PE was proposed by Boneh et al., and they proposed a public key encryption with keyword search (PEKS) scheme that is a special kind of PE [6] . Abdalla et al. introduced anonymous IBE and anonymous HIBE, and they showed that PEKS can be constructed from anonymous IBE [1] . Boneh and Waters constructed a hidden vector encryption (HVE) scheme that support conjunctive equality, conjunctive comparison, and subset queries on encrypted data [10] . Katz et al. constructed the most expressive PE scheme that supports inner product, and they showed that it can support anonymous IBE, HVE, disjunctive operation, evaluation of polynomials, and CNF & DNF queries [23] . Recently, delegatable PE was introduced and it can support anonymous HIBE [29, 25] . We define anonymous HIBE and give the formal definition of its selective security model. Next, we review bilinear groups of prime order, and introduce complexity assumptions for our constructions.

O O O O O O O O O O O O O O B I I O O O O O B I I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Proactive channel access in cognitive radio networks using statistical radio environment maps Inclusion of statistical knowledge of the primary user (PU) channel usage had shown to be beneficial in dynamic spectrum access. Motivated by this fact, this paper investigated the importance of collecting and using statistics on neighboring secondary users (SUs) in selecting channels in addition to the knowledge of PU channel usage. The paper assumed that PU traffic characteristics of the channels are included in the radio environment map in the form of probabilistic suffix trees, which is a sequence predictor based on Markov property. In the proposed method, an intelligent sequence hopping-based common control channel and a carrier sense multiple access (CSMA)/collision avoidance (CA)-based medium access control (MAC) protocol were introduced. As shown in the paper, selecting channels using statistics of both the neighboring SUs and PUs reduced the number of packet collisions compared to a scheme which only uses PU statistics. Furthermore, the simulation results showed that the scheme proposed had better throughput performance with respect to both the random channel selection scheme and the scheme which only uses PU statistics while having less training complexity. The vast growth in wireless applications in the past decade has pushed the FCC to search for efficient spectrum allocation mechanisms to avoid spectrum scarcity [1] . The measurement operation carried out by the FCC's Enforcement Bureau in 2002 gave some insight into the real cause of the widely accepted notion of spectrum scarcity [2] . The recommendations made by the spectrum efficiency working group, based on these findings, consisted of promoting flexible use of spectrum development and deployment of advanced technologies and promoting secondary markets for spectrum [2] . A technology known as cognitive radio (CR) was found to have the capability to implement those recommendations [1] .. However, the secondary usage of spectrum requires the CRs to opportunistically utilize the spatiotemporally 1. The problem of imperfect sensing (false alarms and missed detection) in the physical layer. 2. Unavailability of a common control channel which is always available. 3. Deciding on which channels to sense to find an opportunity efficiently. 4. Efficient selection of a different channel to continue with the communication when the PU appears in the currently used one (spectrum mobility). 5. Efficient sharing of the spectrum with other secondary users (SUs).. Proactive spectrum access (PSA) can be used as a solution for the third and fourth problems mentioned above. Furthermore, using the statistical information on the PU channel usage, highly available sequence hopping (SH)-based common control channels (CCCs) can be designed. One of the solutions to the problem of imperfect sensing is cooperative sensing. In [3] , the authors discuss a semidistributed cooperative spectrum sensing scheme which reduces this problem. In Sections 1.3 and 1.2, we discuss some available solutions for the problems 2 and 5, respectively. This paper is an extension to the work in [4] . In that paper, we only discussed the usage of probabilistic suffix tree (PST) as a prediction mechanism, where there was only a single SU trying to access the channel.. In the scheme presented, the radio environment map (REM) is enhanced with statistical knowledge of the PU channel usage as in [5] . Furthermore, due to PU channels being highly dynamic, it may not be possible to query the REM for information on the current state of the channels. Even if the channels are not highly dynamic, it might not be possible to query the REM before every time slot due to the control channel saturation problem. Considering these facts, in the scheme presented, each transmitting SU queries the REM once for the probabilistic information before the transmission. Then, the SU will cache the probabilistic information to be used until the end of the communication between itself and the receiver while carrying out periodic sensing. In the work, we presented in this paper, the REM learned the PU channel usage distributions from the past channel usage data using a scheme called the PST algorithm [6] , which belongs to the class of variable-order Markov models (VMMs). PSTs only increased the length of the conditioning strings if there was evidence that higher order gives a better prediction result with high probability, although we set a maximum value for the memory in the algorithm [7] . Therefore, the model takes less memory and does not need much bandwidth to transmit. In [8] , it was observed that in some channels, the predictability cannot be improved by increasing the Markov order and the largest improvement for other channels happens when going from zeroth order to first order. But the authors admitted that the predictability did not monotonically decrease with the Markov order. In their results, there were evidence for the existence of channels which are more predictable using higher Markov order, for example, 10% of the channels they tested had an optimal Markov order of 8. Therefore, the PST scheme is a good candidate when a CR operates in diverse wireless channels, because of its selfregulation of the Markov order and the convenience of training. Furthermore, the PST is capable of predicting the channel availability conditioned on a string of past channel observations which are of variable length. The second property mentioned in the above line was very useful in our case since the length of consecutive observations the SUs possessed was varying according to the sensing schedule. In a scenario where a carrier sense multiple access (CSMA)/collision avoidance (CA)-based medium access control (MAC) scheme is used, collisions are inevitable. Therefore, time slots become wasted. To alleviate the network from this issue, we used the statistics collected by each SU on the number of successes and failures on each used channel in a given window of time slots when selecting a set of channels to transmit data. We believe that this is the first time SU channel access data are used in PSA. In this scheme, the channel conditions were not considered. It was ignored to keep the model simple and to clearly show the advantage of using available statistics on PUs and SUs. The channel conditions can be obtained by channel probing after the handshake or from the REM itself, and it will enhance the capacity obtained without doubt. In this paper, we presented a proactive channel access MAC for an ad hoc CR network having a REM for initial setup and showed how this MAC can be used to keep the interference experienced by the PUs at a low level while providing better throughput for the SUs. The novel feature of this research was the incorporation of the SU statistics when selecting a channel set to be used. Furthermore, we introduced a SH CCC and derived the theoretical properties of it. This CCC used PU channel access probabilities when selecting the hopping pattern in the control phase. Then, in the data transmission phase, we used a prediction scheme called the PST which has a very low training and memory complexity. We tested our scheme in a sce- Figure 12 Average number of channels simultaneously used by SUs vs. SU channel access probability for five SUs.

O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Benchmarking sep-CMA-ES on the BBOB-2009 function testbed A partly time and space linear CMA-ES is benchmarked on the BBOB-2009 noiseless function testbed. This algorithm with a multistart strategy with increasing population size solves 17 functions out of 24 in 20-D. The sep-CMA-ES algorithm introduced in [7] is a variant of the covariance matrix adaptation evolution strategy (CMA-ES) [5] that is linear in time and space. This property combined with a faster learning rate makes sep-CMA-ES appropriate for separable function and larger dimensions. A mixed strategy of using sep-CMA-ES and CMA-ES is proposed here and benchmarked on a noiseless function testbed.

B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Hybrid multi-technology routing in heterogeneous vehicular networks Recent developments of wireless communication systems have resulted in the availability of heterogeneous access networks at any geographic area. To make use of this heterogeneous environment for vehicular users to access the Internet, in this article we propose a hybrid multi-technology routing (HMTR) protocol for multihop vehicular networks. HMTR takes into account different combinations of wireless technologies in intermediate hops and is generally formed of a combination of topology-based and position-based routing schemes for packet forwarding. For a given packet, HMTR uses the position-based routing approach over highly variable links whose lifetimes are shorter than the packet expiry time. On the other hand, it employs the topology-based routing approach over more stable links that are expected to stay valid before the expiry time of the packet. Among the candidate routes, any route which does not meet the user requirements in terms of budget or quality of service metrics such as delay and bandwidth is ruled out first. Then, among the remained candidates those with adequate levels of connectivity are assessed for their appropriateness in terms of network utilizations, which are of the network's concern and connection costs, which are of users' concern. Simulation results show that HMTR enables us to achieve the best possible performance in terms of delivery ratio and delivery delay for a given budget, whereas in pure position-based or pure topology-based routing schemes sacrificing the performance or budget may be inevitable in many scenarios. In recent years, various wireless access networks employing different wireless access technologies have been deployed to provide end-users with a wide range of services. As service providers increase the coverage of their access networks, it is more likely that there are overlaps between the coverage areas of different access networks. This situation translates into various connectivity alternatives for end-users, so-called heterogeneity. End-users moving at vehicular speeds a can benefit from such a rich set of connectivity options to access the Internet for a wide range of Internet protocol (IP)-based applications such as email, content delivery, file download, gaming services, IP telephony, and multimedia streaming. In these applications, vehicular nodes equipped with multi-technology radios need to establish an efficient route to the most appropriate attachment point using the most appropriate set of intermediate hops. Attachment points are the interfaces to the core network, such as base stations (BS) in the case of worldwide interoperability for microwave access (WiMAX) or cellular networks, or access points (AP) in the case of wireless local area networks (WLAN), e.g., IEEE 802.11 a/b/g/p WLANs.. b Numerous routing protocols all based on a single wireless technology have been proposed for packet routing in vehicular environments. We refer to this type of protocols as single-technology protocols. In this article, in order to take advantage of the available heterogeneous environment, we study routing protocols that consider the combinations of different wireless technologies in intermediate hops, which we refer to as multi-technology routing protocols.. In a heterogeneous environment it is important to differentiate the problem of packet routing from the problem of optimal access network selection, which has already been extensively studied in the literature [1] [2] [3] [4] [5] [6] [7] . These studies consider the case where end-users are directly covered by several attachment points and decisions should be made to select the most appropriate attachment point for receiving service. However, in a more general case an end-user may not be directly covered by any attachment point or even if an attachment point is available in a single hop, other alternate attachment points could still be preferred. In this case, it is necessary to employ a reliable, robust, and efficient routing protocol that finds the most appropriate attachment point in a larger neighborhood and forwards the packets between the end-user and the attachment point.. Relatively few articles have investigated the issue of multi-technology routing in heterogeneous environments, especially for vehicular networks. In [8] the integration of cellular and WLAN access networks is proposed in which an agent in the cellular network assists the WLAN communications to improve the performance of the network. In [9] cellular and WLAN access networks are combined with the aim of quality of service (QoS) provisioning in a ubiquitous environment. Hung et al. [10] consider a heterogeneous vehicular networking topology in which every end-user can access both WiMAX and WLAN. The end-users' WiMAX radios are to be registered in one WiMAX BS. The BS predicts, in a centralized manner, the positions of all vehicles based on which it computes the most appropriate routes between any two end-users. In all these studies, it is assumed that the access networks with larger coverage areas, e.g., the cellular or WiMAX network, provide global coverage which allows for end-users to directly connect to it at any location at any time. Hence, these networks are used as back-up to provide service at any time when networks with smaller coverage areas such as WLANs are unavailable. Clearly, as the size of vehicular networks may become extremely large in practice, considering such back-up network may not be realistic. Hence, in our heterogeneous topology all access networks regardless of the size of their coverage areas are used as independent connectivity alternatives for multi-hop multi-technology packet forwarding. To the best of our knowledge, none of the previous studies have considered multi-hop multi-technology routing for vehicular networks.. In this article, we consider a vehicular networking environment in which the movements of vehicles are confined by the structure of roads. Since vehicles may move at very high speeds and in different directions, the topology of the network becomes highly dynamic making the design of routing protocols in vehicular environments very challenging. In this regard, many singletechnology routing protocols have been proposed [11] [12] [13] [14] [15] [16] [17] [18] . These routing protocols can be categorized as topology-based and position-based routing protocols. In topology-based routing a complete end-to-end route is established by an appropriate selection of intermediate vehicles before sending the data packets. The downside of single-technology topology-based routing protocols in vehicular environments is that the links are fairly unstable when packets are forwarded over short-range wireless networks such as WLANs. When the transmission range is relatively short relative to the distances vehicles travel over a round-trip time between the source and destination, it is very likely that some intermediate vehicles in the end-to-end route get out of each other's transmission range and the route fails even before any data packet is sent on the route. Some efforts have been made to take the stability of routes into account in the process of establishing them [11] [12] [13] . However, when routes are longer than just a few hops, finding stable end-to-end routes becomes very challenging if not impossible, and in sparse situations it is very likely that an end-to-end route may not even exist due to disconnections. So, position-based routing protocols are gaining popularity.. In position-based routing every relaying vehicle selects the next hop vehicle to forward the packet to on-the-fly based on the position and movement attributes of its one-hop neighbors [14, 15] . The advantage of this type of routing is that the forwarding of packets does not depend on the establishment of an end-to-end route. So, this type of routing is a better choice for highly varying topologies, such as packet forwarding over vehicular networks employing WLAN technology. The downside of this type of protocols is that the forwarding decisions are local and without considering real-time network conditions in terms of connectivity and congestion in other parts of the network. To address these shortcomings, more recent studies have proposed connectivityaware routing schemes [16] [17] [18] . However, in these schemes the connectivity information is pre-determined, and as a result, real-time connectivity and congestion information regarding the parts of the network that are going to be visited in the future is not available. On the other hand, in these schemes the general approach for selecting the most connected route is to make intermediate vehicles report metrics such as average number of neighbors, minimum number of neighbors, and average density of neighbors. Finally, the route with the maximum value of any of these metrics is considered as the most connected route. However, these approaches may not be accurate enough, because even though all these metrics intuitively result in the most connected route, the connectivity in the context of position-based routing is defined as the probability that no disconnection exists along the route. A disconnection is the state where no next hop vehicle can be found along the route, thereby making the communication impossible. To select the route with the maximum connectivity, an approach for calculating the connectivity according to the aforementioned definition is required.. The main idea of our article is to integrate the advantages of topology-based and position-based routing into a unified scheme. Based on the fact that the route instability problem of topology-based routing can be largely overcome using long-range wireless networks such as WiMAX or cellular networks, we propose the hybrid multi-technology routing (HMTR) protocol, which takes a hybrid approach for forwarding packets. In HMTR, topology-based routing is used for forwarding packets over more stable links available in long-range networks, and position-based routing scheme is used for forwarding packets over highly variable links in short-range networks. To determine the stability of a link, we propose a link stability logic which is based on the relative mobility of the vehicles forming the link and the delay requirements of the application involved. As a part of HMTR route selection logic is suggested to prioritize candidate routes based on QoS metrics, network and user preferences and the connectivity of routes. In this regard, we propose a novel microscopic approach for calculating the connectivity of routes on the basis of the connectivity observations of individual vehicles along the routes. To facilitate service delivery in the studied vehicular heterogeneous environment, we also introduce a novel network architecture to address issues such as authentication, authorization, and accounting (AAA) in a multi-operator scenarios. To the best of our knowledge HMTR is the first multi-technology, multi-operator hybrid routing protocol for vehicular communications.. The rest of the article is organized as follows. In the following section, the network topology is introduced, which can be comprised of an arbitrary set of wireless access networks. In Section 3, the HMTR routing protocol is explained. We elaborate on the mechanisms and logics designed for HMTR including the route selection logic and the link stability logic in Section 4. The proposed route connectivity is detailed in Section 5. The performance of HMTR with respect to its different routing possibilities is evaluated in Section 6. Section 7 concludes the article. In this article, we have proposed a routing protocol, HMTR, for Internet access in vehicular networking environments. To make packet forwarding adaptable to the rate of topology changes in the network, in HMTR we use position-based and topology-based routing approaches for packet forwarding over unstable and stable links, respectively. In this regard, we have proposed a link stability logic for evaluating the stability of links. Among the candidate routes, the most appropriate one is obtained by using a two-step route selection logic. The first step is to exclude the routes which do not satisfy the QoS requirements or the budgets of route requesting applications. In the second step, among the remaining candidates, the most connected one is selected in sparse vehicular traffic situations.

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