CSPRC 2017Abstracts

17th Computer SciencePostgraduate Research Colloquium

Claudia Chirita Duncan Mitchell

Royal Holloway University of LondonDepartment of Computer Science

2nd June 2017

Contents

Session 1: Security

BabelView: Evaluating the Impact of Code Injection Attacks inMobile Webviews . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Claudio Rizzo

Cryptobugs: Towards the Automated Enforcement of Crypto-graphic Protocols in JavaScript . . . . . . . . . . . . . . . . . . . 6

Duncan Mitchell

Session 2: Bioinformatics

Functional diversity in metagenomics data and its ethical impli-cations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Sally Radwan

Predicting genes for molecularly uncharacterised diseases . . . . 9Juan Caceres Silva

Signatures of drug side-effects in human phenotype . . . . . . . . 10Diego Galeano Galeano

Gene Pushing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Mateo Torres Bobadilla

Session 3: Machine Learning

On Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Andrej Zukov Gregoric

Autonomous Driving 24/7 . . . . . . . . . . . . . . . . . . . . . . 13Christian Muench

2

Multi-class probabilistic classification using inductive and crossVenn-Abers predictors . . . . . . . . . . . . . . . . . . . . . . . . 14

Valery Manokhin

Session 4: Algorithms, Logics and Languages

On the convergence of Label Propagation Protocols . . . . . . . 15Chhaya Trehan

An institutional approach to computational creativity . . . . . . 16Claudia Elena Chirita

A machine-oriented inference rule format for operational semantics 18Thomas van Binsbergen

Poster presentations

A secure protocol for client-server content negotiation in onlinecommunications . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Emanuele Uliana

Towards efficient and distributed computation of coresets . . . . 21Nery Riquelme Granada

Applying Conformal Predictions on Public BioAssay Data . . . . 22Paolo Toccaceli

Competitive prediction of property prices . . . . . . . . . . . . . 23Raisa Dzhamtyrova

Preface

Welcome to the 17th Computer Science Postgraduate Research Collo-quium (2017). The colloquium serves as a forum for interaction betweenthe faculty, staff and students engaged in the various different disci-plines of research in Computer Science at Royal Holloway Universityof London.

The event is the result of a staff-student collaboration, and hasbeen coordinated by the students. The work presented showcases theinnovative ideas, diversity and excellence of postgraduate research thatis conducted here, within the Department of Computer Science.

We have organized the colloquium into four themes: Security, Bioin-formatics, Machine Learning and Algorithms, Logics and Languages.

Our invited speaker is Prof. John Shawe-Taylor, who has obtaineda PhD in Mathematics at Royal Holloway University of London in1986. Prof. Shawe-Taylor is currently the Director of the Centre forComputational Statistics and Machine Learning at UCL. His mainresearch area is Statistical Learning Theory, but his contributions rangefrom Neural Networks, to Machine Learning, and Graph Theory.

This year, we also have a panel discussion on Deep Learning, chairedby Prof. Jose Luiz Fiadeiro with the participation of Dr. Yuri Kalnishkan,Dr. Zhiyuan Luo, Prof. Kostas Stathis and Prof. Chris Watkins.

Finally, the best presentation and best poster awards are decidedthis year by a committee comprising members of the IT industry: RossDuer (Blackrock), Anthony Eggington (Blackrock), and Kostas Stathis(RHUL).

We hope you enjoy the day, and we thank you for taking part.

2nd June 2017Royal Holloway

ClaudiaDuncan

4

Security

BabelView: Evaluating the Impact of CodeInjection A�acks in Mobile Webviews

Claudio Rizzo

A Webview is a component embedding a full-fledged browser in amobile application. It allows the application to expose specific interfacesto JavaScript code running in the browser to build hybrid applicationsthat mix web technologies with native functions. JavaScript codeinjected into a Webview allows an attacker to abuse this interfaceand possibly manipulate the device or exfiltrate sensitive data. Inthis paper, we present an approach to systematically evaluate thepossible impact of code injection attacks against Webviews. We employstatic information flow analysis on the application instrumented witha model of possible attacker behavior (the BabelView), which makesreasoning about JavaScript semantics unnecessary. We evaluate ourapproach on 10,056 apps from various Android marketplaces, finding1,457 vulnerabilities in 783 apps. Taken together, the apps reported asvulnerable have over 542 million installations worldwide. We manuallyvalidated a random sample of 20 apps and estimate that our fullyautomated analysis achieves a precision of 83%.

5

Security

Cryptobugs: Towards the AutomatedEnforcement of Cryptographic Protocols in

JavaScriptDuncan Mitchell

In the post-Snowden era, ‘encrypt everything’ has become a centralparadigm of handling user data. This in turn has led to the standard-ization of cryptographic libraries and even their inclusion in standardlibraries. However, this has not reduced the technical subtleties ofimplementing cryptographic code. As such, these libraries make iteasy to put cryptography in your program, but still require extensivedomain-specific knowledge to ensure the implementation is correct.Cryptographic bugs caused by this often do not visibly affect the run-time behavior of the program, so are all but impossible for a non-expertdeveloper to detect. They can therefore remain in production code fora prolonged period, damaging any security claims made the developer.

We turn our attention to JavaScript – the near ubiquitous languageof not just the web, but now, with the rise of Node.js, a languageincreasingly popular for client and server-side applications. A series ofquirks and unusual design decisions mean the language itself has proveddifficult to statically analyze – and a full formal semantics has thusfar proved beyond reach. Despite this, the popularity of JavaScript isunwavering.

A methodology for the automated enforcement for the correctness ofthese cryptographic schemes in JavaScript is therefore necessary. Wepropose a runtime-checked system of composable traits which refineJavaScript types and allow for the verification of security properties.We propose to use symbolic execution to systematically generate testcases for the program under test, while generalizing concrete executionsto their entire path. In this talk we outline the approach and describea formalization of the notion of traits, and instantiate through exampletheir usage with examples.

6

Bioinformatics

Functional diversity in metagenomics dataand its ethical implications

Sally Radwan

Metagenomics is the study of the genomes of entire microbial com-munities based on samples obtained directly from the environment andwith little knowledge as to what species might be included in eachsample. The term “environment” is used loosely and indicates anythingthat isn’t a well-defined organism. Examples include soil, ocean water,fresh water, ice, or human (or animal) biomes such as those found inthe gut or the mouth. Those “biomes” are known to host a large varietyof microorganisms which together constitute a genome quite differentfrom that of the host and have been proven to play a significant rolein disease processes and other characteristics such as propensity toobesity or the strength of the immune system.

Existing research on metagenomics focuses primarily on identifyingknown species within the sample and, in some cases, studying theirrelative abundance under certain (normally artificial) stresses such aschemicals or other pollutants. This confinement to known/culturablespecies means that the research relies heavily on existing methods ofanalysis, such as 16S rRNA analysis for species identification, andknown genome assembly algorithms used for standard DNA or RNAanalysis where an organism is known and its genome has been mapped.

This research attempts to reframe the problem and widen the premiseof metagenomics research. The main question posed is: “What happensto a microbial community if it is exposed to certain types of stress?”.The stress can be intermittent, such as a cyclical spike in temperature;temporary, such as a flood; or permanent such as decreased soil moisture.Of particular interest is the change in the “functional diversity” ofthe community due to this stress. Functional diversity is the range offunctions the community is able to perform. This can include metabolicfunctions such as respiration, cell division, or fermentation, or functionsspecific to the environment such as digestion, temperature regulation,aiding in the growth of particular types of plants, etc. A study of thechanges these functions undergo due to stress can shed light on an

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Bioinformatics

environment’s ability to adapt to change as is the case in conditionslike climate change or antibiotic resistance.

This talk will give a brief overview of metagenomics and outline thenew methodology proposed to analyse microbial communities alongwith possible application areas.

8

Bioinformatics

Predicting genes for molecularlyuncharacterised diseases

Juan Caceres Silva

Modern high throughput screening and genome wide associationstudies produce an unprecedented amount data about genetic variations.However, traditional processing techniques are unable to effectivelynarrow the sets of candidate genes associated to disease for extensiveexperimentation.

Effective gene prediction techniques can help in the identificationof the molecular basis of genetic diseases. This characterisation isessential to the diagnosis, prognosis, and therapy development for thesediseases.

We present a general network based disease gene prediction methodfor uncharacterised diseases. Our method uses a novel approach toseed candidate genes to a semi-supervised graph based classificationmethod. Furthermore, the method outperforms state of the art diseasegene prediction and disease module recovery methods.

9

Bioinformatics

Signatures of drug side-e�ects in humanphenotype

Diego Galeano Galeano

My Ph.D. research focuses on developing computational methods fordrug repositioning. Drug repositioning aims at finding new therapeuticindications for known drugs. It can be predicting unknown proteintarget for a drug, new side-effects, or a new disease indication. Methodsin drug repositioning include standard machine learning classifiers,statistical tests, and network-based methods. These methods relymainly on chemical features similarity, on the activity of the drugacross cell lines or on phenotype similarity.

We have developed a latent factor model of drug activity based onside-effects frequency as registered in package inserts. Applying ourmodel to 604 marketed drugs and 2,827 side-effects and indications, weobtained 15 factors which can be used to predict the drug’s molecularactivity, the anatomical therapeutic and chemical (ATC) categoryand even drug-drug interference. In particular, drug-drug signaturesimilarity predicts share ATC class (85% AUC), share drug targets(75.3% AUC), and even drug interference (75.5% AUC).

Our method suggests new uses for old drugs. We study the first-lineantidiabetic drug Metformin prescribe for 150 million people each year.We observed a significant exposure towards carcinogens effects. In the97th percentile of our score for Metformin, we have found terms suchas neoplasm (top 24), neoplasm malignant (top 44), breast cancer (top58) and lung neoplasm malignant (top 103). A recent randomized trialstudy reports that Metformin “improves the prognosis of patients withbreast cancer and diabetes mellitus” (Bratley A., 2017, Nature ReviewsEndocrinology).

Our method also suggests efficacy and safety profile for medicines. Westudy the popular cholesterol-lowering family of drugs Statins, known tobe one of the safest. Nonetheless, we observed exposure of the group toproduce endocrine system disorder as well as neuromuscular effects. Infact, recently FDA (FDANEWS, 2016) “mandates new safety warningfor statins drugs due to risks of memory loss, diabetes, and musclepain.”

10

Bioinformatics

Gene PushingMateo Torres Bobadilla

Protein Function Prediction (PFP) is one of the most challengingtopics within the field of Computational Biology. An inherently complextask, as the very concept of function is hard to define. In fact, severalways of conceptualising function were attempted but remained divergentuntil the creation of the Gene Ontology, a controlled vocabulary of termshierarchically organised with well-defined relations, that became thegold standard for the characterisation of genes. It describes importantaspects of the living cell in three separate domains represented as anuprooted directed acyclic graph, with the more specific terms locatedat the bottom of the ontology. In the context of the Gene Ontology,PFP is the general problem of annotating proteins with GO terms thatcharacterise it.

Advances in sequencing technology resulted in an explosive increasein the number of sequenced genomes. These genes could potentiallybe involved in important biological cell functions and could becomeimportant targets for diagnosis and pharmacological studies. A majorundertaking for biology is therefore to functionally characterise thesegenes.

Usual methods for PFP attempt a genomic scale prediction, assigningGO terms to every gene. Instead, my work focuses on increasing thespecificity of already existing annotations. Simply put, I attempt toassign a more specific GO term to an already annotated protein bychoosing among the children of the current GO term. Making use ofthe structure of the Gene Ontology, I transfer functional information bycomputing semantic similarity across its three domains. The transferredsimilarity is used then to determine which child term is to characterisethe protein. Preliminary results are promising.

11

Machine Learning

On MemoryAndrej Zukov Gregoric

Recurrent neural network (RNN) architectures are often poor atmodelling long distance dependencies in input data. Recent researchhas focused on augmenting RNNs with attention mechanisms whichallow their outputs to depend on a wider context. We briefly cover thehistory and motivation behind attention models and describe the twomain families of attention models: differentiable ‘soft’ attention modelswhich can be backpropagated through and non-differentiable ‘hard’attention models which often require reinforcement learning to train.We propose extensions to existing attention models and showcase ourresults on Facebook’s bAbI tasks.

12

Machine Learning

Autonomous Driving 24/7Christian Muench

In this talk, we will present the DENSE project and some of thework we have done in the previous months. The project is co-funded bythe European Union, the Germany and Daimler AG. It aims to enableautonomous cars to function in all weather conditions, including severeones such as dense fog and heavy rain. This will be accomplished withadvanced sensor technology such as the gated camera which is able toextract accurate depth slices in e.g. heavy fog. Additionally, existingLIDAR technology will be improved by shifting the laser wavelengthto the short-wavelength infrared (SWIR) part of the electromagneticspectrum. Current Lidar systems operate at wavelengths that candamage the human eye. Therefore, laser intensities are always limitedto ensure eye safety. By shifting the wavelengths the laser intensitiescan be adapted more easily to the weather conditions. In our work, wewant to fuse the output of these sensors (optical camera, LIDAR, Radar,gated camera) to obtain a state-of-the-art and robust object detectionalgorithm. Traditional approaches perform so-called late fusion whereeach sensor is evaluated by a separate algorithm. We investigate thefusion of all sensor data using a single convolutional neural network(early fusion). This can be advantageous as the network processes moreinformation such as depth and reflectance which can result in moresophisticated and robust features. One of the main drawbacks of thisapproach might be a significant decrease in performance of the overallnetwork in case one of the sensors is disturbed. The disturbances wewill focus on are unknown to the network. I.e. it has never seen themduring training. We propose a simple data augmentation scheme todemonstrate that the network might be able to handle scenarios whereone sensor channel is highly disturbed by some kind of unknown noisewhile the other channels operate as usual.

13

Machine Learning

Multi-class probabilistic classification usinginductive and cross Venn-Abers predictors

Valery Manokhin

Inductive (IVAP) and cross (CVAP) Venn-Abers predictors are com-putationally efficient algorithms for probabilistic prediction in binaryclassification problems. We present a new approach to multi-classprobability estimation by turning IVAPs and CVAPs into multi-classprobabilistic predictors.

The additional contribution of this paper is a method of usingcalibration techniques for multi-class classification problems via theapplication of the “PKPD” (Price, Knerr, Personnaz and Dreufus)method. This allows for application of well calibrated binary classprobabilities in the multi-class setting in a computationally efficientmanner.

In empirical study of the performance of IVAP and CVAP in themulti-class classification setting, multi-class probability predictors basedon IVAP and CVAP perform well, delivering performance improvementswhen compared to underlying machine-learning classification algorithmssuch as support vector machine, logistic regression and neural networkas well as in comparison with traditional calibration method of Platt’sscaling. The improvements in performance in comparison with theresults produced by underlying algorithms is in line with what has beenreported in the previous literature for binary classification cases, withmax-margin methods such as support vector machine benefiting themost from calibration. In addition, even for well-calibrated algorithmssuch as neural networks and logistic regression, both IVAP and CVAPare often more accurate than the traditional calibration methods.

14

Algorithms, Logics and Languages

On the convergence of Label PropagationProtocols

Chhaya Trehan

Label Propagation Algorithm (LPA) is a widely used mechanismfor distributed community detection in complex networks. AlthoughLPA is known to perform well in empirical studies, its theoreticalperformance guarantees are poorly understood. In this work, we takea first step towards the formal characterization of the performance ofthe LPA by studying its convergence on complete graphs. We showthat the number of rounds until convergence on a complete graphgrows (though extremely slowly) with the size of the graph, whichexplains experimental evidence and disproves the popular belief thatthe convergence is possible in constant number of rounds. We furtherplan to generalize our results to the networks comprised of a collectionof sparsely connected cliques (of different sizes).

15

Algorithms, Logics and Languages

An institutional approach to computationalcreativity

Claudia Elena Chirita

Computational creativity is a subdomain of AI developed in thelast decades to explore the potential of computational systems tocreate original artefacts and ideas. Overlapping with the broaderfield of cognitive science, it encompasses “the philosophy, science andengineering of computational systems which exhibit behaviours thatunbiased observers would deem to be creative”.

We discuss creativity from an algebraic point of view, showing howwe can give a mathematical formalization of creative systems andtheir components. We start from the tenet that creativity can beseen in essence as the identification or location of new conceptualobjects in a conceptual space, and present creative systems in aninstitutional setting. We adopt the understanding of a conceptualspace as an algebraic specification, and develop our study based oninstitution theory. This allows us to maintain the generality of previousdescriptions of creative systems, and at the same time to use formaldefinitions for concept abstraction, concretization, and blending thatenable reasoning about creative processes.

We first define creative systems by means of many-valued specifi-cations and of abstract strategies for the discovery and evaluation ofconcepts based on the notion of graded semantic consequence betweenspecifications. We then focus on a subclass of creative systems modelledas complex dynamic systems and investigate a new connection withservice-oriented architectures, where we regard concepts as modulesand concept discovery as service discovery. In this context, we evaluatethe usefulness of a concept through the mechanism of service selection,and recast concept blending in terms of service binding. This permitsus to study properties of creative processes within the framework ofservice-oriented systems.

While most of the current research in computational creativity seemsto adopt a connectionist view on cognitive and in particular on creativeprocesses, our approach adheres to the computational theory of mind.This opens naturally a series of questions related to the everlasting

16

Algorithms, Logics and Languages

paradigm dispute between the subsymbolic and symbolic views on thephilosophy of mind that one should not ignore. To answer the concernon the origin of concept specifications, we investigate the connectionbetween the abstract representations of concepts in neural networks andtheir algebraic definition. Establishing a formal relation between thetwo would provide a solution: the automatic learning from examplescould alleviate the user’s task of writing complex specifications ofconcepts.

17

Algorithms, Logics and Languages

A machine-oriented inference rule format foroperational semantics

Thomas van Binsbergen

Logical inference rules form a powerful and expressive tool for givinginductive definitions of sets, functions, relations, and other mathe-matical objects. In the context of programming language research,Plotkin’s SOS inference rules define transition systems capturing theoperational semantics of programming languages. By applying the rulesin a systematic fashion, a theoretician forms inductive proofs about thebehaviour of programs, for example to show that a program terminatesor produces certain output. This process is typically automated byengineering an interpreter that finds proofs mechanically.

Theoreticians also proof properties about the semantic specificationof a language itself. For example, that a specification is deterministic,consistent, well-defined, or that it is a conservative extension of anotherspecification. Such proofs are often based on the assertion that all rulesin a specification adhere to a certain rule format.

In this talk, we present a rule format whose restrictions serve adifferent purpose. The rule format is designed to give rules a rela-tively simple and efficient operational interpretation. The operationalinterpretation forms the basis of a generic interpreter, applicable to allvalid specifications, or of a code generation procedure that producesspecialised interpreters. We show that a lower level rule representationadmits program transformations for speeding up the interpretationof rules. We also show that the rules of more expressive formalismscan be translated into the rule format and thus that the rule formatcan be seen as an intermediate language in a pipeline for generatinginterpreters from SOS specifications.

18

Poster presentations

A secure protocol for client-server contentnegotiation in online communications

Emanuele Uliana

Computing devices and network technologies make possible today tocollect, store and analyze massive quantities of data (Terabytes, at thevery least) in a reasonable amount of time. However, Computer Scienceand Computer Engineering need to face their social responsibilities,like other sciences (e.g., Physics, Chemistry and Medicine) had to doin the (recent) past, in order to prevent abuses. In particular, Securityand Privacy are the main features that IT systems and networks musthave today: anything without those non-functional requirements isautomatically (and often dangerously) a complete failure. Securityand Privacy are not independent, and losing one almost always meanslosing the other. With that in mind, assuming a particular definitionof the latter, we identified a list of threat models, alongside with therisks linked to them and the consequences for the parties involved inonline communication(s). We chose to focus on a subset of the problemin a specific domain, which we renamed as the Privacy problem. Afterspecifying it and checking that there are indeed issues with the statusquo, we researched how the existing solutions manage to deal withthem and their limits in doing so.

We propose a solution consisting in a secure protocol for a client-server automatic preliminary negotiation whose objective is to enhancethe privacy for both parties. We designed it with the goal of providinga fairer level of privacy for both parties without forgetting about thesecurity of the communication. We also provide support for the protocolboth client and server side. Our aim during the design phase was tocreate a secure and authenticated channel (phase 1) which can be usedfor a subsequent negotiation (phase 2) of what data both parties areor are not allowed to send to each other and at which conditions. Theresult of the negotiation is either a failure (in which case an erroris returned) or a digital contract specifying rules for the subsequentcommunication (phase 3). The contract produced at the end of phase2 is stored in a tamper-evident common-knowledge environment toprevent damages to its integrity and to provide non-repudiation of the

19

Poster presentations

agreed rules. The communication in phase 3 is a normal client-serverinteraction wrapped in the channel created in phase 1 and subjectedto the rules agreed in phase 2. In particular, phase 3 can employ anyapplication-level protocol, as long as it is compatible with the secureand authenticated channel and does not violate the contract.

20

Poster presentations

Towards e�icient and distributed computationof coresets

Nery Riquelme Granada

Data sets grow in size because they are increasingly being gatheredby information-sensing devices. In the context of millions or evenbillions of data points, proven existing algorithms become unfeasiblesince data sets may not fit in single machines anymore but must bestored in whole clusters of machines.

Coresets are succinct, small summaries of large data sets – so thatsolutions found in the summery are provably competitive with solutionsfound in the full data set. A coreset approximately maintains the sameproperties of the original data set with respect to a specific optimizationproblem. Therefore, running optimization algorithms on the smallcoreset instead of the original data set allow us to compute, underdifferent constraints and definitions of optimality, approximately thesame optimal solutions much faster i.e. a multiplicative “epsilon” istolerated.

One very desirable feature of coresets is that they can be constructedin parallel as well as in a streaming setting where data points arriveone by one, and it is impossible to remember the entire data set due tomemory constrains. The key idea of this feature is that (i) the unionof coresets is a coreset; (ii) computing the coreset of a coreset is stillan approximation to the original data set, but with greater error.

We are interested in proposing a distributed solution for computingcoresets efficiently in parallel and streaming settings by exploitingproperties (i) and (ii) of coresets.

21

Poster presentations

Applying Conformal Predictions on PublicBioAssay Data

Paolo Toccaceli

It can be argued that data sets prevalent in the Chemoinformat-ics domain exhibit a unique combination of size, high-dimensionality,sparseness, and imbalance. This poses challenges that need to bespecifically addressed for a machine learning technique to be applicablewith any chance of success.

We present some preliminary results on the application of MondrianConformal Prediction to a BioAssay data set from the PubChem publicrepository. This effort is part of a recently launched European Project[ExCAPE (Exascale Compound Activity Prediction Engines] aimedat designing and implementing algorithms for predicting compoundbioactivity for the pharmaceutical industry. The specific focus of theproject is on algorithms suitable for extreme parallelization on HighPerformance Computing platforms, with a view to exploiting futureExascale (1018 FLOPS) architectures.

We describe the tools and techniques employed at this preliminarystage in the project to explore the design options for distributing thecomputation and scaling the machine learning algorithms to data setsof hundreds of thousands of samples with hundreds of thousands offeatures. We show the actual results of applying Conformal Prediction(CP) to a data set thought to be representative of typical BioAssaydata. The results serve to illustrate the different degrees of efficiencyobtained with a variety of underlying machine learning algorithms.The results also provide a concrete example of the validity guarantee(rate of errors) that is specific to Conformal Prediction. Finally, wecomplement our presentation of CP confidence prediction approachwith an introduction to the (multi)probabilistic prediction frameworkof Venn Prediction.

22

Poster presentations

Competitive prediction of property pricesRaisa Dzhamtyrova

Accurate property valuation is important for property purchasers, in-vestors and for mortgage-providers to assess credit risk in the mortgagemarket. Valuation models should be able to reflect the most recentinformation. The common approach is to replace old models when thenew data become available. However, sometimes old data can becomerelevant again and it can help us to improve the predictions. As we donot know which data segments can be relevant in the future we need toidentify them dynamically. Although it is natural to assume that thenew model will perform better than the older one, the consideration ofolder observations helps to model the long-term effects in price move-ments. Also when we replace the model with the new one, it makes ourprediction unstable as new predictions will be substantially differentfrom the previous, which is not desirable in commercial models.

For the problem of property valuation we consider methods of sequen-tial prediction with expert advice. We consider the on-line protocolwhere outcomes arrive after we produce our prediction. Althoughprediction of property prices mostly has been investigated in the batchmode, prices are prone to strong movements over time, which naturallysuggests to apply on-line protocol.

We will consider a method based on prediction with expert advicewith specialist experts. This method merges experts strategies basedon their past performance. Our goal is to find a new strategy that willperform almost as well as the best expert. If a specialist expert is notmaking a prediction, we say that it sleeps, otherwise we say that it isawake.

We consider specialist experts as regression models that were builtfor different time intervals. We have found that the quality of pre-dictions does not decrease significantly when we test old models onnew observations. Hence, we can use these specialist experts withsmaller weights to help us to predict new observations. As time passes,new observations become available and more specialist experts becomeawake.

In this framework, we want to compete with the model that was

23

Poster presentations

built using the newest observations and with the best model thatwas built in the batch mode. Our method gives us the improvementcompared to standard predictors and models that were built in thebatch mode using the newest observations. We investigate how thechoice of the prior distribution on the weights of sleeping expertsaffects the performance of the prediction. We have found empiricallythat several distributions that take into consideration the time whenthe specialist expert became awake gives us the better performancecompared to the uniform distribution.

24