Browsing by discipline "Tietojenkäsittelytiede"

With the recent advances in efficient virtualization techniques in using commodity servers cloud computing has emerged as a powerful technology to meet new requirements for supporting a new generation of computing services based on utility model. However barriers to widespread adoption still exists and the dominant platform is yet to be seen in years to come. Hence the challenge of providing scalable cloud infrastructures requires a continuous exploration of new technologies and techniques. This thesis describes an experimental investigation of integrating two such open source technologies, OpenStack and GlusterFS, to build our cloud environment. We designed a number of test case scenarios that help us answer the questions around performance, stability and scalability of the cloud infrastructure deployed. Additionally, the work based on this thesis was accepted to the Conference on Computing in High Energy and Nuclear Physics (CHEP2013), and the paper is due for publishing.

Tämä työ tutkii Clojure-ohjelmointikieltä, joka on erityisesti rinnakkaisohjelmointiin suunniteltu Lisp-kielen murre. Clojure tukee vahvaa liitosta Java-ympäristöön ja sillä kirjoitetut ohjelmat suoritetaan JVM-virtuaalikoneella. Tutkielmassa käydään läpi Lisp-kielten historia, rinnakkaisohjelmoinnin yleiset haasteet ja funktionaalisen ohjelmointiparadigman perusteet. Lisäksi käsitellään Java-kielen ja JVM-virtuaalikoneen ja Clojure-kielen rinnakkaisohjelmointipiirteet. Tutkielman analyysiosassa verrataan Clojuren ja Javan rinnakkaisuusratkaisuja muun muassa tehokkuuden ja käytettävyyden osalta. Clojuren rinnakkaisuusratkaisuista transaktiomuisti osoittautui laskennallisesti hyvin raskaaksi. Lisäksi rinnakkaisratkaisujen lukottomuudesta seuraa se, että tietyt rinnakkaisohjelmointiongelmat ovat hankalia toteuttaa ilman, että käytetään Javan rinnakkaisratkaisuja. Erityisesti synkronisten rinnakkaisratkaisujen osalta kielessä olisi kehittämisen varaa. Javaan verrattuna Clojuren rinnakkaisuusratkaisut ovat hieman yksinkertaisempia käyttää. Tämä johtuu kuitenkin pitkälle Clojuren dynaamisesta tyypityksestä ja funktionaalisesta perusrakenteesta.

Organizations are adopting cloud technologies at an increasing rate. Significant share of growth of cloud deployments is coming from application migrations to cloud computing. Migrating existing legacy applications to cloud computing platform is not a trivial task. A migration methodology will help migrating applications to cloud more effectively and with lower risk than doing it by trial and error. A part of the cloud migration process is the selection and execution of a migration strategy amongst the possible, situational and commonly used options. The migration strategy defines many of the migration process activities since they depend on cloud architecture and service and deployment models, which are implicitly set by the migration strategy. Many of the existing cloud migration methods don’t specify the factors that lead to migration strategy selection. The migration strategy selection is a critical part of migration planning involving multiple organisations and several individuals. This thesis presents categories of migration strategy factors derived from a cloud migration methodology and process framework review and validates the factors by doing a deductive thematic analysis against qualitative case study interview data. By having a clarity and a way to address the migration strategy factors, will increase the migration success rate and reduce planning time.

Ohjelmointikielen valinta on tärkeä osa ohjelmistoprojektien toteutusta. Vaikka ohjelmointikielet uudistuvat nopeaan tahtiin, nykypäivänä on yhä tavallista valita ohjelmiston toteutukseen C-ohjelmointikieli, joka on standardoitu yli 30 vuotta sitten. Tutkielmassa tutkitaan syitä, miksi C on nykypäivänä vieläkin laajassa käytössä uudempien ohjelmointikielten sijaan. Tutkielmassa C:hen verrattaviksi ohjelmointikieliksi valitaan Ada, C++, D, Go sekä Rust. Kaikki viisi kieltä ovat tehokkaita. Tämän lisäksi jokaisen kielten historiassa on ollut tavoitteena korvata C:n käyttö. Ohjelmointikieliä verrataan C:hen suorituskyvyn, muistinkäytön sekä C-yhteensopivuuden osalta. Tämän lisäksi tutkielmassa selvitetään tärkeimpiä C:n ominaisuuksia sekä C:n kehitettävissä olevia ominaisuuksia. Tuloksia käytetään uuden Purkka-ohjelmointikielen suunnitteluun. Muut ohjelmointikielet todetaan suorituskykymittauksissa C:tä hitaammiksi. Tämän lisäksi muiden ohjelmointikielten ominaisuudet, kuten automaattisen muistinhallinnan, todetaan aiheuttavan ongelmia C-yhteensopivuudelle. C:n tärkeimmiksi ominaisuuksiksi nousevat esiin yksinkertaisuus, tehokkuus sekä alustariippumattomuus. Nämä ominaisuudet otetaan huomioon Purkka-kielen suunnittelussa, jossa painotetaan näiden lisäksi yhteensopivuutta C-ohjelmointikielen kanssa. Tutkielmaa varten kehitetty Purkka-kieli on suunniteltu C:n kaltaiseksi ohjelmointikieleksi, jossa on muutettu C:n syntaksia yksinkertaisemmaksi ja johdonmukaisemmaksi. Suorituskykymittauksissa todetaan, että Purkan muutokset C:hen eivät aiheuta suoritusaikaisia rasitteita. Koska Purkka-kieli käännetään C:ksi, se on mahdollisimman yhteensopiva nykyisten kääntäjien kanssa.

In the Internet age, malware poses a serious threat to information security. Many studies have been conducted on using machine learning for detecting malicious software. Although major breakthroughs have been achieved in this area, the problem has not been completely eradicated. In this thesis, we are going through the concept of utilizing machine learning for malware detection and conduct several experiments with two different classifiers (Support Vector Machine and Naive Bayes) to compare their ability to detect malware based on Port-able Executable (PE) file format headers. A malware classifier dataset built with header field values of portable executable files was obtained from GitHub and used for experimental part of the thesis. We conducted 5 different experiments with several different trial settings. Various statistical methods have been used to assess the significance of the results. The first and second experiment show that using SVM and Naive Bayes classification methods for our dataset can result in high sensitivity rate. In the rest of the experiments, we focus on ac-curacy rate of both classifiers with different settings. The results show that although there were no big differences in the accuracy rates of the classifiers, the value of variance of ac-curacy rates is greater in Naive Bayes than in SVM. The study investigates ability of two different methods to classify information in their distinctive way. It also provides evidences that show that the learning-based approach provides a means for accurate automated analysis of malware behavior which helps in the struggle against malicious software.

I reklam används sloganer för att förbättra återkallandet av den annonserade produkten av konsumenter och skilja den från andra på marknaden. Att skapa effektiva slagord är en resurskrävande uppgift för människor. I denna avhandling beskriver vi en ny metod för att automatiskt generera sloganer, med tanke på ett målkoncept (t ex bil) och en adjektivsegenskap för att uttrycka (t ex elegant) som input. Dessutom föreslår vi en metod för att generera nominella metaforer med hjälp av en metafor-tolkningsmodell för att möjliggöra generering av metaforiska slagord. Metoden för att generera sloganer extraherar skelett från befintliga sloganer, så fyller det ett skelett med lämpliga ord genom att använda flera språkliga resurser (som ett förvar av grammatiska och semantiska relationer och språkmodeller) och genetiska algoritmer samtidigt som man optimerar flera mål såsom semantiska relateradhet, språkkorrigering och användning av retoriska enheter. Vi utvärderar metaforen och slogangenereringsmetoderna med hjälp av en tänktalkoplattform. På en 5-punkts Likert-skala ber vi online-domare att bedöma de genererade metaforerna tillsammans med tre andra metaforer som genererades med andra metoder och visa hur bra de kommunicerar den eftersökta betydelsen. Slogangenereringsmetoden utvärderas genom att be crowdsourced-domare att bedöma genererade sloganer från fem perspektiv, vilka är 1) hur bra är sloganet relaterat till ämnet, 2) hur korrekt är sloganets språk, 3) hur metaforiskt är sloganet, 4) hur engagerande, attraktivt och minnesvärt är det och 5) hur bra är sloganet överlag. Dessa frågor är utvalda för att undersöka effekterna av relateradhet till produkten och den markerade egenskapen, användningen av retoriska anordningar och språkets korrekthet på den övergripande uppskattningen av slogan. På samma sätt utvärderar vi befintliga sloganer som har skapats av äkta människor. Baserat på utvärderingarna analyserar vi metoden som helhet tillsammans med de enskilda optimeringsfunktionerna och ger insikter om befintliga sloganer. Resultaten från våra utvärderingar visar att vår metaforgeneringsmetod kan producera lämpliga metaforer. För slogangenereraren bevisar resultaten att metoden har varit framgångsrik i att producera minst en effektiv slogan för varje utvärderad input. Ändå finns det utrymme för att förbättra metoden, som diskuteras i slutet av avhandlingen.

The Internet of Things (IoT) consists of physical devices, such as temperature sensors and lights, that are connected to the Internet. The devices are typically battery powered and are constrained by their low processing power, memory and low bitrate wireless communication links. The vast amount of IoT devices can cause heavy congestion in the Internet if congestion is not properly addressed. The Constrained Application Protocol (CoAP) is an HTTP-like protocol for constrained devices built on top of UDP. CoAP includes a simple congestion control algorithm (DefaultCoAP). CoAP Simple Congestion Control/Advanced (CoCoA) is a more sophisticated alternative for DefaultCoAP. CoAP can also be run over TCP with TCP's congestion control mechanisms. The focus of this thesis is to study CoAP's congestion control. Shortcomings of DefaultCoAP and CoCoA are identified using empirical performance evaluations conducted in an emulated IoT environment. In a scenario with hundreds of clients and a large buffer in the bottleneck router, DefaultCoAP does not adapt to the long queuing delay. In a similar scenario where short-lived clients exchange only a small amount of messages, CoCoA clients are unable to sample a round-trip delay time. Both of these situations are severe enough to cause a congestion collapse, where most of the link bandwidth is wasted on unnecessary retransmissions. A new retransmission timeout and congestion control algorithm called Fast-Slow Retransmission Timeout (FASOR) is congestion safe in these two scenarios and is even able to outperform CoAP over TCP. FASOR with accurate round-trip delay samples is able to outperform basic FASOR in the challenging and realistic scenario with short-lived clients and an error-prone link.

BitTorrent is one of the most used file sharing protocols on the Internet today. Its efficiency is based on the fact that when users download a part of a file, they simultaneously upload other parts of the file to other users. This allows users to efficiently distribute large files to each other, without the need of a centralized server. The most popular torrent site is the Pirate Bay with more than 5,700,000 registered users. The motivation for this research is to find information about the use of BitTorrent, especially on the Pirate Bay website. This will be helpful for system administrators and researchers. We collected data on all of the torrents uploaded to the Pirate Bay from 25th of December, 2010 to 28th of October, 2011. Using this data we found out that a small percentage of users are responsible for a large portion of the uploaded torrents. There are over 81,000 distinct users, but the top nine publishers have published more than 16% of the torrents. We examined the publishing behaviour of the top publishers. The top usernames were publishing so much content that it became obvious that there are groups of people behind the usernames. Most of the content published is video files with a 52% share. We found out that torrents are uploaded to the Pirate Bay website at a fast rate. About 92% of the consecutive uploads have happened within 100 seconds or less from each other. However, the publishing activity varies a lot. These deviations in the publishing activity may be caused by down time of the Pirate Bay website, fluctuations in the publishing activity of the top publishers, national holidays or weekdays. One would think that the publishing activity with so many independent users would be quite level, but surprisingly this is not the case. About 85% of the files of the torrents are less than 1.5 GB in size. We also discovered that torrents of popular feature films were uploaded to the Pirate Bay very fast after their release and the top publishers appear to be competing on who releases the torrents first. It seems like the impact of the top publishers is quite significant in the publishing of torrents.

Computer programs are common in our daily environment and new robotic devices emerge constantly. One of the key skills for the future is understanding the general purpose as well as intricate details of both concepts. Programs written in conventional programming languages are purely text with syntax cryptic to an unexperienced observer and errors can easily be made in the writing process, which is far from ideal basis to introduce programming or robotics to children on elementary school level. We set to investigate how a visual learning environment could reduce this chasm between the skills of the young and the perceived difficulty of computer technology and with what assortment of technologies and working methods such platforms, both programming and robotic, could be produced in a project with multiple parties. A joint endeavor was started between two research groups in different universities and the education services of a municipality in Finland. In the project, a visual programming environment was created where the users can assemble programs by stacking and manipulating colorful blocks to form a command flow. The set of functions are traditional with calculations, variables and loops, but also include commands to pilot a robotic platform with wheels, arms and a facial expression indicating display. This robot is a product of the project also, based on a commercial design. The venture can be concluded a success. The learning environment and the roving robot were created in the course of the project and architectural design and selected technologies proved sensible. The empirical exploration accumulated knowledge for academic research.

We study the use of data collected via electroencephalography (EEG) to classify stimuli presented to subjects using a variety of mathematical approaches. We report an experiment with three objectives: 1) To train individual classifiers that reliably infer the class labels of visual stimuli using EEG data collected from subjects; 2) To demonstrate brainsourcing, a technique to combine brain responses from a group of human contributors each performing a recognition task to determine classes of stimuli; 3) To explore collaborative filtering techniques applied to data produced by individual classifiers to predict subject responses for stimuli in which data is unavailable or otherwise missing. We reveal that all individual classifier models perform better than a random baseline, while a brainsourcing model using data from as few as four participants achieves performance superior to any individual classifier. We also show that matrix factorization applied to classifier outputs as a collaborative filtering approach achieves predictive results that perform better than random. Although the technique is fairly sensitive to the sparsity of the dataset, it nonetheless demonstrates a viable proof-of-concept and warrants further investigation.