Browsing by discipline "Datavetenskap"

Animaatiotekniikat tarjoavat tehokkaita menetelmiä hahmojen ja jäykkien kappaleiden animoimiseen. Tutkielmassa kuvataan yleisemmät 3D-animaatiotekniikat unohtamatta muita edeltäneitä tekniikoita, tutustutaan grafiikkaluikuhihnaan ja luurankomalliin sekä käydään läpi peliresurssien hallintaan, luontiin, käsittelyyn ja talletukseen liittyviä asioita. Lisäksi tutkielmassa tutustutaan animaatiojärjestelmän jäsentelyyn ohjelmistoarkkitehtuurin näkökulmasta sekä esitellään Unreal- ja Unity-pelimoottorit ja niiden animaatiojärjestelmät. Tutkielman konstruktiivisessa osuudessa kuvataan molemmissa pelimoottoreissa toteutetut esimerkkisovellukset ja toteutukseen liittyvät kokemukset sekä tehdään kokemuksiin perustuva vertaileva analyysi.

De-anonymization is an important requirement in real-world V2X systems (e.g., to enable effective law enforcement). In de-anonymization, a pseudonymous identity is linked to a long-term identity in a process known as pseudonym resolution. For de-anonymization to be acceptable from political, social and legislative points of view, it has to be accountable. A system is accountable if no action by it or using it can be taken without some entity being responsible for the action. Being responsible for an action means that the responsible entity cannot deny its responsibility of or relation to an action afterwards. The main research question is: How can we achieve accountable pseudonym resolution in V2X communication systems? One possible answer is to develop an accountable de-anonymization service, which is compatible with existing V2X pseudonym schemes. The accountability can be achieved by making some entities accountable for the de-anonymization. This thesis proposes a system design that enables, i) fine-grained pseudonym resolution; ii) the possibility to inform the subject of the resolution after a suitable time delay; and iii) the possibility for the public to audit the aggregate number of pseudonym resolutions. A TEE is used to ensure these accountability properties. The security properties of this design are verified using symbolic protocol analysis.

Managing a telecommunications network requires collecting and processing a large amount of data from the base stations. The current method used by the infrastructure providers is hierarchical and it has significant performance problems. As the amount of traffic within telecommunications networks is expected to continue increasing rapidly in the foreseeable future, these performance problems will become more and more severe. This thesis outlines a distributed publish/subscribe solution that is designed to replace the current method used by the infrastructure providers. In this thesis, we propose an intermediate layer between the base stations and the network management applications which will be built on top of Apache Kafka. The solution will be qualitatively evaluated from different aspects. ACM Computing Classification System (CCS): Networks -> Network management Networks -> Network architectures

A measurement for how similar (or distant) two computer programs are has a wide range of possible applications. For example, they can be applied to malware analysis or analysis of university students' programming exercises. However, as programs may be arbitrarily structured, capturing the similarity of two non-trivial programs is a complex task. By extracting call graphs (graphs of caller-callee relationships of the program's functions, where nodes denote functions and directed edges denote function calls) from the programs, the similarity measurement can be changed into a graph problem. Previously, static call graph distance measures have been largely based on graph matching techniques, e.g. graph edit distance or maximum common subgraph, which are known to be costly. We propose a call graph distance measure based on features that preserve some structural information from the call graph without explicitly matching user defined functions together. We define basic properties of the features, several ways to compute the feature values, and give a basic algorithm for generating the features. We evaluate our features using two small datasets: a dataset of malware variants, and a dataset of university students' programming exercises, focusing especially on the former. For our evaluation we use experiments in information retrieval and clustering. We compare our results for both datasets to a baseline, and additionally for the malware dataset to the results obtained with a graph edit distance approximation. In our preliminary results we show that even though the feature generation approach is simpler than the graph edit distance approximation, the generated features can perform on a similar level as the graph edit distance approximation. However, experiments on larger datasets are still required to verify the results.

Expected benefits from agile methodologies to project success have encouraged organizations to extend agile approaches to areas they were not originally intended to such as large scale information systems projects. Research regarding agile methods in large scale software development projects have existed for few years and it is considered as its own research area. This study investigates agile methods on the large scale software development and information systems projects and its goal is to produce more understanding of agile methods suitability and the conditions under which they would most likely contribute to project success. The goal is specified with three research questions; I) what are the characteristics specific to large scale software engineering projects or large scale Information Systems project, II) what are the challenges caused by these characteristics and III) how agile methodologies mitigate these challenges? In this study resent research papers related to the subject are investigated and characteristics of large scale projects and challenges associated to them are recognized. Material of the topic was searched starting from the conference publications and distributions sites related to the subject. Collected information is supplemented with the analysis of project characteristics against SWEBOK knowledge areas. Resulting challenge categories are mapped against agile practises promoted by Agile Alliance to conclude the impact of practises to the challenges. Study is not a systematics literature review. As a result 6 characteristics specific to large scale software development and IS projects and 10 challenge categories associated to these characteristics are recognized. The analysis reveals that agile practises enhance the team level performance and provide direct practises to manage challenges associated to high amount of changes and unpredictability of software process both characteristic to a large scale IS project but challenges still remain on the cross team and overall project level. As a conclusion it is stated that when seeking the process model with agile approach which would respond to all the characteristics of large scale project thus adding the likelihood of project success adaptations of current practises and development of additional practises are needed. To contribute this four areas for adaptations and additional practises are suggested when scaling agile methodologies over large scale project contexts; 1) adaptation of practises related to distribution, assignment and follow up of tasks, 2) alignment of practises related to software development process, ways of working and common principles over all teams, 3) developing additional practises to facilitate collaboration between teams, to ensure interactions with the cross functional project dimensions and to strengthen the dependency management and decision making between all project dimensions and 4) possibly developing and aligning practises to facilitate teams' external communication. Results of the study are expected to be useful for software development and IS project practitioners when considering agile method adoptions or adaptations in a large scale project context. ACM Computing Classification System (CCS) 2012: - Social and professional topics~Management of computing and information systems - Software and its engineering~Software creation and management

Tutkielmassa arvioidaan aktoripohjaisten rinnakkaistamisratkaisujen soveltuvuutta pelimoottoreihin. Tutkielmassa esitellään ensin pelimoottoreiden ja aktoripohjaisen rinnakkaisuuden perusperiaatteet ja sitten aktoripohjainen Stage-pelimoottoritoteutus. Tutkielman lopuksi tutkitaan Stage-moottorin tehokkuutta ja helppokäyttöisyyttä verrattuna perinteisiä lukkopohjaisia rinnakkaistamisratkaisuja hyödyntävään pelimoottoriin.

This thesis is a study on Lean Startup metrics. It attempts to answer what is measured and how in Lean Startup, how it differs from other software measurement and what information needs today's startups have. This study has literature study and an empirical survey using real start-ups. This study explains how the software measurement has changed over the years and what kind of metrics Lean Startup suggests to be used and why. It shows differences in measurement use between traditional start-ups and Lean Startups. This study suggest reasons and motivations to use measurement in start-ups and gives examples when not to. In the scope of this study a survey with questionnaires and interviews was conducted. It showed distinctly different attitudes towards measurement between traditional start-up entrepreneurs and those who like to call themselves Lean Startup entrepreneurs. Measurement in Lean Startup is not an end in itself, but a useful tool for gaining feedback for the gut-feelings of an entrepreneur. Metrics, when meaningful and correct, communicate the focus within a start-up and will objectively evaluate the business' success.

Cloud computing offers important resources, performance, and services nowadays when it has became popular to collect, store and analyze large data sets. This thesis builds on Berkeley Data Analysis Stack (BDAS) as a cloud computing environment designed for Big Data handling and analysis. Especially two parts of the BDAS, the cluster resource manager Mesos and the distribution manager Spark will be introduced. They offer important features, such as efficiency, multi-tenancy, and fault tolerance, for cloud computing. The Spark system expands MapReduce, the well-known cloud computing paradigm. Machine learning algorithms can predict trends and anomalies of large data sets. This thesis will present one of them, a distributed decision tree algorithm, implemented on the Spark system. As an example case, the decision tree will be used on the versatile energy consumption data from mobile devices, such as smart phones and tablets, of the Carat project. The data consists of information about the usage of the device, such as which applications have been running, network connections, battery temperatures, and screen brightness, for example. The decision tree aims to find chains of data features that might lead to energy consumption anomalies. Results of the analysis can be used to advise users on how to improve their battery life. This thesis will present selected analysis results together with advantages and disadvantages of the decision tree analysis.

Software defect prediction is the process of improving software testing process by identifying defects in the software. It is accomplished by using supervised machine learning with software metrics and defect data as variables. While the theory behind software defect prediction has been validated in previous studies, it has not widely been implemented into practice. In this thesis, a software defect prediction framework is implemented for improving testing process resource allocation and software release time optimization at RELEX Solutions. For this purpose, code and change metrics are collected from RELEX software. The used metrics are selected with the criteria of their frequency of usage in other software defect prediction studies, and availability of the metric in metric collection tools. In addition to metric data, defect data is collected from issue tracker. Then, a framework for classifying the collected data is implemented and experimented on. The framework leverages existing machine learning algorithm libraries to provide classification functionality, using classifiers which are found to perform well in similar software defect prediction experiments. The results from classification are validated utilizing commonly used classifier performance metrics, in addition to which the suitability of the predictions is verified from a use case point of view. It is found that software defect prediction does work in practice, with the implementation achieving comparable results to other similar studies when measuring by classifier performance metrics. When validating against the defined use cases, the performance is found acceptable, however the performance varies between different data sets. It is thus concluded that while results are tentatively positive, further monitoring with future software versions is needed to verify performance and reliability of the framework.

The number of devices connected to the Internet is growing exponentially. These devices include smartphones, tablets, workstations and Internet of Things devices, which offer a number of cost and time savings by automating routine tasks for the users. However, these devices also introduce a number of security and privacy concerns for the users. These devices are connected to small office/home-office (SOHO) and enterprise networks, where users have very little to no information about threats associated to these devices and how these devices can be managed properly to ensure user's privacy and data security. We proposed a new platform to automate the security and management of the networks providing connectivity to billions of connected devices. Our platform is low cost, scalable and easy to deploy system, which provides network security and management features as a service. It is consisted of two main components i.e. Securebox and Security and Management Service (SMS). Securebox is a newly designed Openflow enabled gateway residing in edge networks and is responsible for enforcing the security and management decisions provided by SMS. SMS runs a number of traffic analysis services to analyze user traffic on demand for Botnet, Spamnet, malware detection. SMS also supports to deploy on demand software based middleboxes for on demand analysis of user traffic in isolated environment. It handles the configuration update, load balancing and scalability of these middlebox deployments as well. In contrast to current state of the art, the proposed platform offloads the security and management tasks to an external entity, providing a number of advantages in terms of deployment, management, configuration updates and device security. We have tested this platform in real world scenarios. Evaluation results show that the platform can be efficiently deployed in traditional networks in an incremental manner. It also allows us to achieve similar user experience with security features embedded in the connectivity.