Endre søk
Begrens søket
1234567 1 - 50 of 6427
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Treff pr side
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
Merk
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Aalto, Erik
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Learning Playlist Representations for Automatic Playlist Generation2015Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Spotify is currently the worlds leading music streaming ser-vice. As the leader in music streaming the task of providing listeners with music recommendations is vital for Spotify. Listening to playlists is a popular way of consuming music, but traditional recommender systems tend to fo-cus on suggesting songs, albums or artists rather than pro-viding consumers with playlists generated for their needs.

    This thesis presents a scalable and generalizeable approach to music recommendation that performs song selection for the problem of playlist generation. The approach selects tracks related to a playlist theme by finding the charac-terizing variance for a seed playlist and projects candidate songs into the corresponding subspace. Quantitative re-sults shows that the model outperforms a baseline which is taking the full variance into account. By qualitative results the model is also shown to outperform professionally curated playlists in some cases.

    Fulltekst (pdf)
    fulltext
  • 2.
    Aarno, Daniel
    KTH, Skolan för datavetenskap och kommunikation (CSC), Numerisk Analys och Datalogi, NADA.
    Intention recognition in human machine collaborative systems2007Licentiatavhandling, monografi (Annet vitenskapelig)
    Abstract [sv]

    Robotsystem har använts flitigt under de senaste årtiondena för att skapa automationslösningar i ett flertal områden. De flesta nuvarande automationslösningarna är begränsade av att uppgifterna de kan lösa måste vara repetitiva och förutsägbara. En av anledningarna till detta är att dagens robotsystem saknar förmåga att förstå och resonera om omvärlden. På grund av detta har forskare inom robotik och artificiell intelligens försökt att skapa intelligentare maskiner. Trots att stora framsteg har gjorts då det gäller att skapa robotar som kan fungera och interagera i en mänsklig miljö så finns det för nuvarande inget system som kommer i närheten av den mänskliga förmågan att resonera om omvärlden.

    För att förenkla problemet har vissa forskare föreslagit en alternativ lösning till helt självständiga robotar som verkar i mänskliga miljöer. Alternativet är att kombinera människors och maskiners förmågor. Exempelvis så kan en person verka på en avlägsen plats, som kanske inte är tillgänglig för personen i fråga på grund av olika orsaker, genom att använda fjärrstyrning. Vid fjärrstyrning skickar operatören kommandon till en robot som verkar som en förlängning av operatörens egen kropp.

    Segmentering och identifiering av rörelser skapade av en operatör kan användas för att tillhandahålla korrekt assistans vid fjärrstyrning eller samarbete mellan människa och maskin. Assistansen sker ofta inom ramen för virtuella fixturer där eftergivenheten hos fixturen kan justeras under exekveringen för att tillhandahålla ökad prestanda i form av ökad precision och minskad tid för att utföra uppgiften.

    Den här avhandlingen fokuserar på två aspekter av samarbete mellan människa och maskin. Klassificering av en operatörs rörelser till ett på förhand specificerat tillstånd under en manipuleringsuppgift och assistans under manipuleringsuppgiften baserat på virtuella fixturer. Den specifika tillämpningen som behandlas är manipuleringsuppgifter där en mänsklig operatör styr en robotmanipulator i ett fjärrstyrt eller samarbetande system.

    En metod för att följa förloppet av en uppgift medan den utförs genom att använda virtuella fixturer presenteras. Istället för att följa en på förhand specificerad plan så har operatören möjlighet att undvika oväntade hinder och avvika från modellen. För att möjliggöra detta estimeras kontinuerligt sannolikheten att operatören följer en viss trajektorie (deluppgift). Estimatet används sedan för att justera eftergivenheten hos den virtuella fixturen så att ett beslut om hur rörelsen ska fixeras kan tas medan uppgiften utförs.

    En flerlagers dold Markovmodell (eng. layered hidden Markov model) används för att modellera mänskliga färdigheter. En gestemklassificerare som klassificerar en operatörs rörelser till olika grundläggande handlingsprimitiver, eller gestemer, evalueras. Gestemklassificerarna används sedan i en flerlagers dold Markovmodell för att modellera en simulerad fjärrstyrd manipuleringsuppgift. Klassificeringsprestandan utvärderas med avseende på brus, antalet gestemer, typen på den dolda Markovmodellen och antalet tillgängliga träningssekvenser. Den flerlagers dolda Markovmodellen tillämpas sedan på data från en trajektorieföljningsuppgift i 2D och 3D med en robotmanipulator för att ge både kvalitativa och kvantitativa resultat. Resultaten tyder på att den flerlagers dolda Markovmodellen är väl lämpad för att modellera trajektorieföljningsuppgifter och att den flerlagers dolda Markovmodellen är robust med avseende på felklassificeringar i de underliggande gestemklassificerarna.

    Fulltekst (pdf)
    FULLTEXT01
  • 3.
    Aarno, Daniel
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Sommerfeld, Johan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Kragic, Danica
    KTH, Skolan för datavetenskap och kommunikation (CSC), Datorseende och robotik, CVAP. KTH, Skolan för datavetenskap och kommunikation (CSC), Centra, Centrum för Autonoma System, CAS.
    Pugeault, Nicolas
    Kalkan, Sinan
    Woergoetter, Florentin
    Krüger, Norbert
    Early reactive grasping with second order 3D feature relations2008Inngår i: Recent Progress In Robotics: Viable Robotic Service To Human / [ed] Lee, S; Suh, IH; Kim, MS, 2008, Vol. 370, s. 91-105Konferansepaper (Fagfellevurdert)
    Abstract [en]

    One of the main challenges in the field of robotics is to make robots ubiquitous. To intelligently interact with the world, such robots need to understand the environment and situations around them and react appropriately, they need context-awareness. But how to equip robots with capabilities of gathering and interpreting the necessary information for novel tasks through interaction with the environment and by providing some minimal knowledge in advance? This has been a longterm question and one of the main drives in the field of cognitive system development. The main idea behind the work presented in this paper is that the robot should, like a human infant, learn about objects by interacting with them, forming representations of the objects and their categories that are grounded in its embodiment. For this purpose, we study an early learning of object grasping process where the agent, based on a set of innate reflexes and knowledge about its embodiment. We stress out that this is not the work on grasping, it is a system that interacts with the environment based on relations of 3D visual features generated trough a stereo vision system. We show how geometry, appearance and spatial relations between the features can guide early reactive grasping which can later on be used in a more purposive manner when interacting with the environment.

  • 4. Aasi, Parisa
    et al.
    Nunes, Ivan
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Data- och systemvetenskap, DSV.
    Rusu, Lazar
    Hodosi, Georg
    Does Organizational Culture Matter in IT Outsourcing Relationships?2015Inngår i: 2015 48TH HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS), IEEE Computer Society, 2015, s. 4691-4699Konferansepaper (Fagfellevurdert)
    Abstract [en]

    IT Outsourcing (ITO) is used widely by Multinational Companies (MNCs) as a sourcing strategy today. ITO relationship between service buyer and provider then becomes a crucial issue in achieving expected objectives. This research sheds light on the influence of organizational culture (OC) of the buyer company on its ITO relationship with the provider. More specifically, the influence that OC can have on four significant dimensions of trust, cooperation, communication and commitment in ITO is studied through a qualitative analysis. IT managers of six MNCs were interviewed which exposed the connection between OC and ITO relationship factors. An open communication culture, speed of adaption to change, receiving innovative solutions, flat or hierarchical structures and responsibility degree appeared as the most visible differences between OCs of MNCs influencing ITO relationships. The results can be used for improving the ITO by considering the influence of OC to gain more benefits from outsourcing.

  • 5.
    Abbas, Haider
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektroniksystem.
    Options-Based Security-Oriented Framework for Addressing Uncerainty Issues in IT Security2010Doktoravhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    Continuous development and innovation in Information Technology introduces novel configuration methods, software development tools and hardware components. This steady state of flux is very desirable as it improves productivity and the overall quality of life in societies. However, the same phenomenon also gives rise to unseen threats, vulnerabilities and security concerns that are becoming more critical with the passage of time. As an implication, technological progress strongly impacts organizations’ existing information security methods, policies and techniques, making obsolete existing security measures and mandating reevaluation, which results in an uncertain IT infrastructure. In order to address these critical concerns, an options-based reasoning borrowed from corporate finance is proposed and adapted for evaluation of security architecture and decision- making to handle them at organizational level. Options theory has provided significant guidance for uncertainty management in several domains, such as Oil & Gas, government R&D and IT security investment projects. We have applied options valuation technique in a different context to formalize optimal solutions in uncertain situations for three specific and identified uncertainty issues in IT security. In the research process, we formulated an adaptation model for expressing options theory in terms useful for IT security which provided knowledge to formulate and propose a framework for addressing uncertainty issues in information security. To validate the efficacy of this proposed framework, we have applied this approach to the SHS (Spridnings- och Hämtningssystem) and ESAM (E-Society) systems used in Sweden. As an ultimate objective of this research, we intend to develop a solution that is amenable to automation for the three main problem areas caused by technological uncertainty in information security: i) dynamically changing security requirements, ii) externalities caused by a security system, iii) obsoleteness of evaluation. The framework is general and capable of dealing with other uncertainty management issues and their solutions, but in this work we primarily deal with the three aforementioned uncertainty problems. The thesis presents an in-depth background and analysis study for a proposed options-based security-oriented framework with case studies for SHS and ESAM systems. It has also been assured that the framework formulation follows the guidelines from industry best practices criteria/metrics. We have also proposed how the whole process can be automated as the next step in development.

  • 6.
    Abbas, Haider
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Data- och systemvetenskap, DSV.
    Threats and Security Measures Involved in VoIP-Telephony2006Independent thesis Advanced level (degree of Master (One Year)), 30 poäng / 45 hpOppgave
  • 7.
    Abbas, Haider
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektronik- och datorsystem, ECS.
    Sundkvist, Stefan
    KTH, Skolan för informations- och kommunikationsteknik (ICT).
    Increasing the Performance of Crab Linux Router Simulation Package Using XEN2006Inngår i: IEEE International Conference on Industrial and Information Systems, Kandy, Sri Lanka, 2006, s. 459-462Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Nowadays hardware components are very expensive, especially if the prime purpose is to perform some routing related lab exercises. Physically connected network resources are required to get the desired results. Configuration of network resources in a lab exercise consumes much time of the students and scientists. The router simulation package Crab(1), based on KnoppW, Quagga' and User Mode Linux (UML) is designed for the students to facilitate them in performing lab exercises on a standalone computer where no real network equipment is needed. In addition to that it provides the facility of connection with the real network equipments. Crab also handles the pre configuration of different parts of the simulated networks like automatic IT addressing etc. This paper will describe the performance enhancing of Crab by replacing User Mode Linux virtual machine with XEN capable of providing ten virtual sessions concurrently using a standalone computer.

  • 8.
    Abbas, Haider
    et al.
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektroniksystem.
    Yngström, Louise
    Hemani, Ahmed
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektroniksystem.
    Empowering Security Evaluation of IT Products with Options Theory2009Inngår i: 30th IEEE Symposium on Security & Privacy, Oakland, USA, 2009Konferansepaper (Fagfellevurdert)
  • 9.
    Abbas, Zainab
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS.
    Al-Shishtawy, Ahmad
    RISE SICS, Stockholm, Sweden.
    Girdzijauskas, Sarunas
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS. RISE SICS, Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS.
    Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks2018Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Short-term traffic prediction allows Intelligent Transport Systems to proactively respond to events before they happen. With the rapid increase in the amount, quality, and detail of traffic data, new techniques are required that can exploit the information in the data in order to provide better results while being able to scale and cope with increasing amounts of data and growing cities. We propose and compare three models for short-term road traffic density prediction based on Long Short-Term Memory (LSTM) neural networks. We have trained the models using real traffic data collected by Motorway Control System in Stockholm that monitors highways and collects flow and speed data per lane every minute from radar sensors. In order to deal with the challenge of scale and to improve prediction accuracy, we propose to partition the road network into road stretches and junctions, and to model each of the partitions with one or more LSTM neural networks. Our evaluation results show that partitioning of roads improves the prediction accuracy by reducing the root mean square error by the factor of 5. We show that we can reduce the complexity of LSTM network by limiting the number of input sensors, on average to 35% of the original number, without compromising the prediction accuracy.

  • 10.
    Abbas, Zainab
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS.
    Kalavri, Vasiliki
    Systems Group, ETH, Zurich, Switzerland.
    Carbone, Paris
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS.
    Vlassov, Vladimir
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS.
    Streaming Graph Partitioning: An Experimental Study2018Inngår i: Proceedings of the VLDB Endowment, E-ISSN 2150-8097, Vol. 11, nr 11, s. 1590-1603Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Graph partitioning is an essential yet challenging task for massive graph analysis in distributed computing. Common graph partitioning methods scan the complete graph to obtain structural characteristics offline, before partitioning. However, the emerging need for low-latency, continuous graph analysis led to the development of online partitioning methods. Online methods ingest edges or vertices as a stream, making partitioning decisions on the fly based on partial knowledge of the graph. Prior studies have compared offline graph partitioning techniques across different systems. Yet, little effort has been put into investigating the characteristics of online graph partitioning strategies.

    In this work, we describe and categorize online graph partitioning techniques based on their assumptions, objectives and costs. Furthermore, we employ an experimental comparison across different applications and datasets, using a unified distributed runtime based on Apache Flink. Our experimental results showcase that model-dependent online partitioning techniques such as low-cut algorithms offer better performance for communication-intensive applications such as bulk synchronous iterative algorithms, albeit higher partitioning costs. Otherwise, model-agnostic techniques trade off data locality for lower partitioning costs and balanced workloads which is beneficial when executing data-parallel single-pass graph algorithms.

  • 11.
    Abbas, Zainab
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Programvaruteknik och datorsystem, SCS.
    Sottovia, Paolo
    Huawei Munich Research Centre, Munich, Germany.
    Hassan, Mohamad Al Hajj
    Huawei Munich Research Centre, Munich, Germany.
    Foroni, Daniele
    Huawei Munich Research Centre, Munich, Germany.
    Bortoli, Stefano
    Huawei Munich Research Centre, Munich, Germany.
    Real-time Traffic Jam Detection and Congestion Reduction Using Streaming Graph Analytics2020Inngår i: 2020 IEEE International Conference on Big Data (Big Data), Institute of Electrical and Electronics Engineers (IEEE) , 2020, s. 3109-3118Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Traffic congestion is a problem in day to day life, especially in big cities. Various traffic control infrastructure systems have been deployed to monitor and improve the flow of traffic across cities. Real-time congestion detection can serve for many useful purposes that include sending warnings to drivers approaching the congested area and daily route planning. Most of the existing congestion detection solutions combine historical data with continuous sensor readings and rely on data collected from multiple sensors deployed on the road, measuring the speed of vehicles. While in our work we present a framework that works in a pure streaming setting where historic data is not available before processing. The traffic data streams, possibly unbounded, arrive in real-time. Moreover, the data used in our case is collected only from sensors placed on the intersections of the road. Therefore, we investigate in creating a real-time congestion detection and reduction solution, that works on traffic streams without any prior knowledge. The goal of our work is 1) to detect traffic jams in real-time, and 2) to reduce the congestion in the traffic jam areas.In this work, we present a real-time traffic jam detection and congestion reduction framework: 1) We propose a directed weighted graph representation of the traffic infrastructure network for capturing dependencies between sensor data to measure traffic congestion; 2) We present online traffic jam detection and congestion reduction techniques built on a modern stream processing system, i.e., Apache Flink; 3) We develop dynamic traffic light policies for controlling traffic in congested areas to reduce the travel time of vehicles. Our experimental results indicate that we are able to detect traffic jams in real-time and deploy new traffic light policies which result in 27% less travel time at the best and 8% less travel time on average compared to the travel time with default traffic light policies. Our scalability results show that our system is able to handle high-intensity streaming data with high throughput and low latency.

  • 12.
    Abbasi, Azad Ismail
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Coffeepot for Masochists: A Study in User-Centered System Design2015Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    This master thesis is carried out in the field of “Human-Computer interaction”, more specifically the area “User-centered system design”. The focus has been on “usability” and useful graphical user interfaces. Current theories and definitions in the field have been considered. Literature studies contain well known authors and organisations in domains mentioned above; Jakob Nielsen, Donald A Norman and International Organization for Standardization ISO to mention some.

     Another source for this work from which the theories and way of working have been used is the book “User-Centered System Design” written by Jan Gulliksen and Bengt Göransson.

     The work started with a literature study followed by looking at methods to use. The next step was to do task and user analysis which followed by the development phase. The user has been given a central role in this project and, just as recommended, also been involved through the whole cycle. A useful method to get feedback from users, in addition to interviews and workshops, has been the “Heuristic Evaluation”.

     The final result and conclusion shows that the user-centered system design is a powerful tool to adapt when designing and developing interactive user interface.

    Fulltekst (pdf)
    Azad Abbasi - Master Thesis
  • 13. Abbaszadeh Shahri, A.
    et al.
    Larsson, Stefan
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Jord- och bergmekanik.
    Renkel, C.
    Artificial intelligence models to generate visualized bedrock level: a case study in Sweden2020Inngår i: Modeling Earth Systems and Environment, ISSN 2363-6203, E-ISSN 2363-6211, Vol. 6, nr 3, s. 1509-1528Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Assessment of the spatial distribution of bedrock level (BL) as the lower boundary of soil layers is associated with many uncertainties. Increasing our knowledge about the spatial variability of BL through high resolution and more accurate predictive models is an important challenge for the design of safe and economical geostructures. In this paper, the efficiency and predictability of different artificial intelligence (AI)-based models in generating improved 3D spatial distributions of the BL for an area in Stockholm, Sweden, were explored. Multilayer percepterons, generalized feed-forward neural network (GFFN), radial based function, and support vector regression (SVR) were developed and compared to ordinary kriging geostatistical technique. Analysis of the improvement in progress using confusion matrixes showed that the GFFN and SVR provided closer results to realities. The ranking of performance accuracy using different statistical errors and precision/recall curves also demonstrated the superiority and robustness of the GFFN and SVR compared to the other models. The results indicated that in the absence of measured data the AI models are flexible and efficient tools in creating more accurate spatial 3D models. Analyses of confidence intervals and prediction intervals confirmed that the developed AI models can overcome the associated uncertainties and provide appropriate prediction at any point in the subsurface of the study area. 

  • 14. Abd El Ghany, M. A.
    et al.
    El-Moursy, M. A.
    Ismail, Mohammed
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Elektroniksystem. Ohio State University, Columbus, United States .
    High throughput architecture for high performance NoC2009Inngår i: ISCAS: 2009 IEEE International Symposium on Circuits and Systems, IEEE , 2009, s. 2241-2244Konferansepaper (Fagfellevurdert)
    Abstract [en]

    High Throughput Butterfly Fat Tree (HTBFT) architecture to achieve high performance Networks on Chip (NoC) is proposed. The architecture increases the throughput of the network by 38% while preserving the average latency. The area of HTBFT switch is decreased by 18% as compared to Butterfly Fat Tree switch. The total metal resources required to implement HTBFT design is increased by 5% as compared to the total metal resources required to implement BFT design. The extra power consumption required to achieve the proposed architecture is 3% of the total power consumption of the BFT architecture.

  • 15.
    Abdallah Hussein Mohammed, Ahmed
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Analyzing common structures in Enterprise Architecture modeling notations2022Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Over the past few decades, the field of Enterprise Architecture has attracted researchers, and many Enterprise Architecture modeling frameworks have been proposed. However, in order to support the different needs, the different frameworks offer many different elements types that can be used to create an Enterprise Architecture. This abundance of elements can make it difficult for the end-user to differentiate between the usages of all the various elements in order to identify what elements they actually need. Therefore, this research analyzes existing Enterprise Architecture modeling frameworks and extract the common properties that exists in the different Enterprise Architecture modeling notations. In this study, we performed a Systematic Literature Review that aims at finding the most commonly used Enterprise Architecture modeling frameworks in the Enterprise Architecture literature. Additionally, the elements defined in these frameworks are used to create a taxonomy based on the similarities between the different Enterprise Architecture Frameworks. Our results showed that TOGAF, ArchiMate, DoDAF, and IAF are the most used modeling frameworks. Also, we managed to identify the common elements that are available in the different Enterprise Architecture Frameworks mentioned above and represent the common elements in a multilevel model. The findings of this study can make it easier for the end-user to pick the appropriate elements for their use cases, as it highlights the core elements of Enterprise Architecture modeling. Additionally, we showed how our model can be extended to support the needs of different domains. This thesis also forms the foundation for the development of an Enterprise Architecture modeling framework that can be customized and extended so that only the relevant elements are presented to the end-user.

    Fulltekst (pdf)
    fulltext
  • 16.
    Abdelgalil, Mohammed Saqr
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Människocentrerad teknologi, Medieteknik och interaktionsdesign, MID.
    Lopez-Pernas, Sonsoles
    Idiographic Learning Analytics:A single student (N=1) approach using psychological networks2021Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Recent findings in the field of learning analytics have brought to our attention that conclusions drawn from cross-sectional group-level data may not capture the dynamic processes that unfold within each individual learner. In this light, idiographic methods have started to gain grounds in many fields as a possible solution to examine students’ behavior at the individual level by using several data points from each learner to create person-specific insights. In this study, we introduce such novel methods to the learning analytics field by exploring the possible potentials that one can gain from zooming in on the fine-grained dynamics of a single student. Specifically, we make use of Gaussian Graphical Models —an emerging trend in network science— to analyze a single student's dispositions and devise insights specific to him/her. The results of our study revealed that the student under examination may be in need to learn better self-regulation techniques regarding reflection and planning.

  • 17.
    Abdelgalil, Mohammed Saqr
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Människocentrerad teknologi, Medieteknik och interaktionsdesign, MID. University of Eastern Finland, Joensuu, Finland.
    López-Pernas, S.
    Idiographic learning analytics: A single student (N=1) approach using psychological networks2021Inngår i: CEUR Workshop Proceedings, CEUR-WS , 2021, s. 16-22Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Recent findings in the field of learning analytics have brought to our attention that conclusions drawn from cross-sectional group-level data may not capture the dynamic processes that unfold within each individual learner. In this light, idiographic methods have started to gain grounds in many fields as a possible solution to examine students' behavior at the individual level by using several data points from each learner to create person-specific insights. In this study, we introduce such novel methods to the learning analytics field by exploring the possible potentials that one can gain from zooming in on the fine-grained dynamics of a single student. Specifically, we make use of Gaussian Graphical Models -an emerging trend in network science- to analyze a single student's dispositions and devise insights specific to him/her. The results of our study revealed that the student under examination may be in need to learn better self-regulation techniques regarding reflection and planning. 

  • 18.
    Abdelmassih, Christian
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Container Orchestration in Security Demanding Environments at the Swedish Police Authority2018Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    The adoption of containers and container orchestration in cloud computing is motivated by many aspects, from technical and organizational to economic gains. In this climate, even security demanding organizations are interested in such technologies but need reassurance that their requirements can be satisfied. The purpose of this thesis was to investigate how separation of applications could be achieved with Docker and Kubernetes such that it may satisfy the demands of the Swedish Police Authority.

    The investigation consisted of a literature study of research papers and official documentation as well as a technical study of iterative creation of Kubernetes clusters with various changes. A model was defined to represent the requirements for the ideal separation. In addition, a system was introduced to classify the separation requirements of the applications.

    The result of this thesis consists of three architectural proposals for achieving segmentation of Kubernetes cluster networking, two proposed systems to realize the segmentation, and one strategy for providing host-based separation between containers. Each proposal was evaluated and discussed with regard to suitability and risks for the Authority and parties with similar demands. The thesis concludes that a versatile application isolation can be achieved in Docker and Kubernetes. Therefore, the technologies can provide a sufficient degree of separation to be used in security demanding environments.

    Fulltekst (pdf)
    fulltext
  • 19.
    Abdelnour, Jerome
    et al.
    NECOTIS Dept. of Electrical and Computer Engineering, Sherbrooke University, Canada.
    Rouat, Jean
    NECOTIS Dept. of Electrical and Computer Engineering, Sherbrooke University, Canada.
    Salvi, Giampiero
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH. Department of Electronic Systems, Norwegian University of Science and Technology, Norway.
    NAAQA: A Neural Architecture for Acoustic Question Answering2022Inngår i: IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, s. 1-12Artikkel i tidsskrift (Fagfellevurdert)
    Fulltekst (pdf)
    fulltext
  • 20.
    Abdihakim, Ali
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Characterizing Feature Influence and Predicting Video Popularity on YouTube2021Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    YouTube is an online video sharing platform where users can distribute and consume video and other types of content. The rapid technological advancement along with the proliferation och technological gadgets has led to the phenomenon of viral videos where videos and content garner hundreds of thousands if not million of views in a short span of time. This thesis looked at the reason for these viral content, more specifically as it pertains to videos on YouTube. This was done by building a predictor model using two different approaches and extracting important features that causes video popularity. The thesis further observed how the subsequent features impact video popularity via partial dependency plots. The knn model outperformed logistic regression model. The thesis showed, among other things that YouTube channel and title were the most important features followed by comment count, age and video category. Much research have been done pertaining to popularity prediction, but less on deriving important features and evaluating their impact on popularity. Further research has to be conduced on feature influence, which is paramount to comprehend the causes for content going viral. 

    Fulltekst (pdf)
    fulltext
  • 21.
    Abdlwafa, Alan
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Edman, Henrik
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Distributed Graph Mining: A study of performance advantages in distributed data mining paradigms when processing graphs using PageRank on a single node cluster2015Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    Distributed data mining is a relatively new area within computer science that is steadily growing, emerging from the demands of being able to gather and process various distributed data by utilising clusters. This report presents the properties of graph structured data and what paradigms to use for efficiently processing the data type, based on comprehensive theoretical studies applied on practical tests performed on a single node cluster. The results in the study showcase the various performance aspects of processing graph data, using different open source paradigm frameworks and amount of shards used on input. A conclusion to be drawn from this study is that there are no real performance advantages to using distributed data mining paradigms specifically developed for graph data on single machines. 

    Fulltekst (pdf)
    fulltext
  • 22.
    Abdul Khader, Shahbaz
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL. ABB Future Labs, CH-5405 Baden, Switzerland..
    Yin, Hang
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Falco, Pietro
    ABB Corp Res, S-72178 Västerås, Sweden..
    Kragic, Danica
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Data-Efficient Model Learning and Prediction for Contact-Rich Manipulation Tasks2020Inngår i: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, nr 3, s. 4321-4328Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this letter, we investigate learning forward dynamics models and multi-step prediction of state variables (long-term prediction) for contact-rich manipulation. The problems are formulated in the context of model-based reinforcement learning (MBRL). We focus on two aspects-discontinuous dynamics and data-efficiency-both of which are important in the identified scope and pose significant challenges to State-of-the-Art methods. We contribute to closing this gap by proposing a method that explicitly adopts a specific hybrid structure for the model while leveraging the uncertainty representation and data-efficiency of Gaussian process. Our experiments on an illustrative moving block task and a 7-DOF robot demonstrate a clear advantage when compared to popular baselines in low data regimes.

  • 23.
    Abdulaziz Ali Haseeb, Mohamed
    KTH, Skolan för datavetenskap och kommunikation (CSC), Robotik, perception och lärande, RPL.
    Passive gesture recognition on unmodified smartphones using Wi-Fi RSSI2017Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    The smartphone is becoming a common device carried by hundreds of millions of individual humans worldwide, and is used to accomplish a multitude of different tasks like basic communication, internet browsing, online shopping and fitness tracking. Limited by its small size and tight energy storage, the human-smartphone interface is largely bound to the smartphones small screens and simple keypads. This prohibits introducing new rich ways of interaction with smartphones.

     

    The industry and research community are working extensively to find ways to enrich the human-smartphone interface by either seizing the existing smartphones resources like microphones, cameras and inertia sensors, or by introducing new specialized sensing capabilities into the smartphones like compact gesture sensing radar devices.

     

    The prevalence of Radio Frequency (RF) signals and their limited power needs, led us towards investigating using RF signals received by smartphones to recognize gestures and activities around smartphones. This thesis introduces a solution for recognizing touch-less dynamic hand gestures from the Wi-Fi Received Signal Strength (RSS) received by the smartphone using a recurrent neural network (RNN) based probabilistic model. Unlike other Wi-Fi based gesture recognition solutions, the one introduced in this thesis does not require a change to the smartphone hardware or operating system, and performs the hand gesture recognition without interfering with the normal operation of other smartphone applications.

     

    The developed hand gesture recognition solution achieved a mean accuracy of 78% detecting and classifying three hand gestures in an online setting involving different spatial and traffic scenarios between the smartphone and Wi-Fi access points (AP). Furthermore the characteristics of the developed solution were studied, and a set of improvements have been suggested for further future work.

    Fulltekst (pdf)
    fulltext
  • 24.
    Abenius, Erik
    KTH, Tidigare Institutioner (före 2005), Numerisk analys och datalogi, NADA.
    Time-Domain Inverse Electromagnetic Scattering using FDTD and Gradient-based Minimization2004Licentiatavhandling, monografi (Annet vitenskapelig)
    Abstract [en]

    The thesis addresses time-domain inverse electromagneticscattering for determining unknown characteristics of an objectfrom observations of the scattered .eld. Applications includenon-destructive characterization of media and optimization ofmaterial properties, for example the design of radar absorbingmaterials.A nother interesting application is the parameteroptimization of subcell models to avoid detailed modeling ofcomplex geometries.

    The inverse problem is formulated as an optimal controlproblem where the cost function to be minimized is thedi.erence between the estimated and observed .elds, and thecontrol parameters are the unknown object characteristics. Theproblem is solved in a deterministic gradient-basedoptimization algorithm using a parallel 2D FDTD scheme for thedirect problem.This approach is computationally intensive sincethe direct problem needs to be solved in every optimizationiteration in order to compute an estimated .eld.H ighlyaccurate analytical gradients are computed from the adjointformulation.In addition to giving better accuracy than .nitedi.erences, the analytical gradients also have the advantage ofonly requiring one direct and one adjoint problem to be solvedregardless of the number of parameters.

    When absorbing boundary conditions are used to truncate thecomputational domain, the equations are non-reversible and theentire time-history of the direct solution needs to be storedfor the gradient computation.Ho wever, using an additionaldirect simulation and a restart procedure it is possible tokeep the storage at an acceptable level.

    The inverse method has been successfully applied to a widerange of industrial problems within the European project,IMPACT (Inverse Methods for Wave Propagation Applications inTime-Domain).T he results presented here includecharacterization of layered dispersive media, determination ofparameters in subcell models for thin sheets and narrow slotsand optimization problems where the observed .eld is given bydesign objectives.

    Fulltekst (pdf)
    FULLTEXT01
  • 25.
    Abensour Sellström, Gabriel
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Runefelt Tõnisson, Meidi
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Analysis of Voting Algorithms: a comparative study of the Single Transferable Vote.2012Independent thesis Advanced level (professional degree), 10 poäng / 15 hpOppgave
    Abstract [en]

    A voting system is defined as a procedure through which political power is distributed among candidates - from the ballot box to the parliament. This essay specifically seeks to contrast the Single Transferable Vote system with two other voting algorithms (Modified Sainte-Laguë and First-Past-The-Post), by constructing Java implementations of the algorithms and running example data through them. Thus, the suitability of a possible real-life implementation of the Single Transferable Vote method in a Swedish parliament context is evaluated. Furthermore, an alternative version of the original STV method which has been modified to fit these conditions is suggested. The effects of such an implementation on election outcomes are not entirely conclusive, and the conclusion is that more research is needed before a definite evaluation can be made.

  • 26.
    Aboode, Adam
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Anomaly Detection in Time Series Data Based on Holt-Winters Method2018Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    In today's world the amount of collected data increases every day, this is a trend which is likely to continue. At the same time the potential value of the data does also increase due to the constant development and improvement of hardware and software. However, in order to gain insights, make decisions or train accurate machine learning models we want to ensure that the data we collect is of good quality. There are many definitions of data quality, in this thesis we focus on the accuracy aspect.

    One method which can be used to ensure accurate data is to monitor for and alert on anomalies. In this thesis we therefore suggest a method which, based on historic values, is able to detect anomalies in time series as new values arrive. The method consists of two parts, forecasting the next value in the time series using Holt-Winters method and comparing the residual to an estimated Gaussian distribution.

    The suggested method is evaluated in two steps. First, we evaluate the forecast accuracy for Holt-Winters method using different input sizes. In the second step we evaluate the performance of the anomaly detector when using different methods to estimate the variance of the distribution of the residuals. The results indicate that the suggested method works well most of the time for detection of point anomalies in seasonal and trending time series data. The thesis also discusses some potential next steps which are likely to further improve the performance of this method.

    Fulltekst (pdf)
    fulltext
  • 27.
    Abou Zliekha, M.
    et al.
    Damascus University/Faculty of Information Technology.
    Al Moubayed, Samer
    Damascus University/Faculty of Information Technology.
    Al Dakkak, O.
    Higher Institute of Applied Science and Technology (HIAST).
    Ghneim, N.
    Higher Institute of Applied Science and Technology (HIAST).
    Emotional Audio-Visual Arabic Text to Speech2006Inngår i: Proceedings of the XIV European Signal Processing Conference (EUSIPCO), Florence, Italy, 2006Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The goal of this paper is to present an emotional audio-visual. Text to speech system for the Arabic Language. The system is based on two entities: un emotional audio text to speech system which generates speech depending on the input text and the desired emotion type, and un emotional Visual model which generates the talking heads, by forming the corresponding visemes. The phonemes to visemes mapping, and the emotion shaping use a 3-paramertic face model, based on the Abstract Muscle Model. We have thirteen viseme models and five emotions as parameters to the face model. The TTS produces the phonemes corresponding to the input text, the speech with the suitable prosody to include the prescribed emotion. In parallel the system generates the visemes and sends the controls to the facial model to get the animation of the talking head in real time.

  • 28.
    Abourraja, Mohamed Nezar
    et al.
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Hälsoinformatik och logistik.
    Kringos, Nicole
    KTH, Skolan för arkitektur och samhällsbyggnad (ABE), Byggvetenskap, Bro- och stålbyggnad.
    Meijer, Sebastiaan
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Hälsoinformatik och logistik.
    Proposal of a module-driven architecture for building simulation models devoted to container terminals: dilemmas in applying generic, flexible, and modular principles2023Inngår i: Simulation (San Diego, Calif.), ISSN 0037-5497, E-ISSN 1741-3133, Vol. 99, nr 7, s. 703-727Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Container terminals are complex systems where containerized cargo undergoes a set of processing and handling operations to be delivered to their outgoing modes. A pool of decision support methods and simulation models has been developed to assist planners and managers in making decisions about daily operations. Nevertheless, most are designed for a particular terminal and not generic types. Indeed, a generic model serves as a conceptual factory to create specific models which greatly reduces the time and efforts of development; however, building such a model is no mean feat. To this aim, the paper on hand discusses the complexity of applying genericity, flexibility, and modularity in system modeling and proposes a generic architecture to build modular and flexible simulation models for container terminals. This architecture is split into a set of smaller, manageable, well-connected, and generic modules that facilitate the creation of highly parametrized specific models. An illustrative example of the architecture usage is presented in a case study, the new container terminal of Stockholm, and the resulting models were validated by subject matter experts. Finally, to prove its efficiency, a numerical study fed with real data is conducted to investigate the handling capacity of the studied system under different handling and flow scenarios. The obtained results show that the terminal handling capacity can be increased by around 50% if three to four more straddle carriers are added to the existing fleet.

  • 29.
    Abrahamsson, Felix
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Designing a Question Answering System in the Domain of Swedish Technical Consulting Using Deep Learning2018Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Question Answering systems are greatly sought after in many areas of industry. Unfortunately, as most research in Natural Language Processing is conducted in English, the applicability of such systems to other languages is limited. Moreover, these systems often struggle in dealing with long text sequences.

    This thesis explores the possibility of applying existing models to the Swedish language, in a domain where the syntax and semantics differ greatly from typical Swedish texts. Additionally, the text length may vary arbitrarily. To solve these problems, transfer learning techniques and state-of-the-art Question Answering models are investigated. Furthermore, a novel, divide-and-conquer based technique for processing long texts is developed.

    Results show that the transfer learning is partly unsuccessful, but the system is capable of perform reasonably well in the new domain regardless. Furthermore, the system shows great performance improvement on longer text sequences with the use of the new technique.

    Fulltekst (pdf)
    fulltext
  • 30. Abrahamsson, M.
    et al.
    Sundberg, Johan
    KTH, Skolan för datavetenskap och kommunikation (CSC), Tal, musik och hörsel, TMH, Musikakustik.
    Subglottal pressure variation in actors’ stage speech2007Inngår i: Voice and Gender Journal for the Voice and Speech Trainers Association / [ed] Rees, M., VASTA Publishing , 2007, s. 343-347Kapittel i bok, del av antologi (Fagfellevurdert)
  • 31.
    Acharya, Jaldeep
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Fröberg, Ludvig
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    A comparison of interfaces in choice driven games: Investigating possible future applications of NLIs in choice driven games by comparing a menu- based interface with an NLI in a text-based game2016Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    Natural language processing has for a long time been a field of research and has been regarded as a thing of the future. Due to its complexity it stopped being featured in computer games in the early 2000s. It has however had a recent revival as a consequence of advancements made in speech recognition, making the possible applications of natural language processing much larger. One market that hasn’t seen much in the way of natural language interfaces recently is that of computer games. This report covers the basics of natural language processing needed to implement two versions of a simple text-based adventure game, one with a menu-based interface and one with a natural lan- guage interface. These were then played by a test group from which usability statistics were gathered to determine if it is likely that NLP will find its way back in to choice driven games in the future.

    The results showed that even though the menu-based interface has a faster rate of progression, the NLI version of the game was perceived as more enjoyable by users with experience in gaming. The reason being that the NLI al- lowed for more thinking on the user’s part and therefore the game presented a greater challenge, something that is perceived as attractive by users with experience in com- puter games. Also the measured usability was roughly the same for both interfaces while it was feared that it would be much lower for NLIs. Therefore, the conclusion was that it is highly plausible that NLI will find its way back into the gaming world, since it adds a new dimension to adventure games, which is something that attracts users. However, this is given that NLP development continues in the same fast pace as it is today, making it possible to implement a more accurate NLI. 

    Fulltekst (pdf)
    fulltext
  • 32.
    Acin, Medya
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Stansvik, Elvis
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Improving Player Engagement inTetris Through EDR Monitoring2013Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    When designing computer games, one is often interested in evoking feelings of

    engagement, enjoyment and challenge in the player. One way of doing so is

    dynamically adjusting the difficulty of the game. Traditionally, this adjustment

    has been based on the performance of the player. However, in recent years there

    has been an increased interest in dynamically adjusting the difficulty level of a

    game based on physiological signals from the player. In this Bachelor’s project,

    we have studied the effect of using an electrodermal activity (EDA) wristband

    sensor as the source signal for the difficulty adjustment algorithm and compared

    it to the traditional approach of using the performance of the player.

    We developed two Tetris games, one EDA controlled and one performance controlled,

    and let participants play them both. Each game session was followed

    by a questionnaire. Our results show that, although participants reported an

    increased sense of engagement and challenge when playing the EDA version,

    further research is necessary before the usefulness of EDA in this setting can be

    established.

    Fulltekst (pdf)
    Improving Player Engagement in
  • 33.
    Ackland, Patrik
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Fast and Scalable Static Analysis using Deterministic Concurrency2017Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    This thesis presents an algorithm for solving a subset of static analysis data flow problems known as Interprocedural Finite Distribute Subset problems. The algorithm, called IFDS-RA, is an implementation of the IFDS algorithm which is an algorithm for solving such problems. IFDS-RA is implemented using Reactive Async which is a deterministic, concurrent, programming model. The scalability of IFDS-RA is compared to the state-of-the-art Heros implementation of the IFDS algorithm and evaluated using three different taint analyses on one to eight processing cores. The results show that IFDS-RA performs better than Heros when using multiple cores. Additionally, the results show that Heros does not take advantage of multiple cores even if there are multiple cores available on the system.

    Fulltekst (pdf)
    fulltext
  • 34.
    Adamsson, Johan
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Run-time specialization for compiled languages using online partial evaluation2024Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Partial evaluation is a program transformation technique that specializes a program with respect to part of its input. While the specialization is typically performed ahead-of-time, moving it to a later stage may expose additional opportunities and allow for faster residual programs to be constructed. In this thesis, we present a method for specializing programs at run-time, for compiled code, using an online partial evaluator. Although partial evaluation has several applications, the evaluation of the method primarily focuses on its performance benefits. The main research problem addressed in this thesis is that of incorporating an online partial evaluator in compiled code. The partial evaluator is a sourceto-source translator that takes and produces an abstract syntax tree (AST). Our approach consists of three parts, namely that of partially evaluating, obtaining a partially evaluable representation and run-time code emitting. Concretely, we use the concept of lifting to store an AST in the compiled code that the partial evaluator then specializes at run-time. The residual code is thereafter naively just-in-time (JIT) compiled through dynamically linking it back to the executable as a shared library. We evaluate the method on several programs and show that the specialized programs sometimes are faster even with a low recursion depth. Though, while the results are promising, the overhead is typically significant and therefore the break-even points are large. Further research, for example using an efficient JIT compiler, is required to better evaluate the performance benefits of the approach.

    Fulltekst (pdf)
    fulltext
  • 35.
    Adamsson, Marcus
    et al.
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Vorkapic, Aleksandar
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    A comparison study of Kd-tree, Vp-tree and Octree for storing neuronal morphology data with respect to performance2016Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    In this thesis we investigated performance of Kdtree, Vptree and Octree for storing neuronal morphology data. Two naive list structures were implemented to compare with the space partition data structures. The performance was measured with different sizes of neuronal networks and different types of test cases. A comparison with focus on cache misses, average search time and memory usage was made. Furthermore, measurements gathered quantitative data about each data structure. The results showed significant difference in performance of each data structure. It was concluded that Vptree is more suitable for searches in smaller populations of neurons and for specific nodes in larger populations, while Kdtree is better for volume searches in larger populations. Octree had highest average search time and memory requirement.

    Fulltekst (pdf)
    fulltext
  • 36.
    Adhi, Boma
    et al.
    RIKEN, Ctr Computat Sci R CCS, Wako, Saitama, Japan..
    Cortes, Carlos
    RIKEN, Ctr Computat Sci R CCS, Wako, Saitama, Japan..
    Tan, Yiyu
    RIKEN, Ctr Computat Sci R CCS, Wako, Saitama, Japan..
    Kojima, Takuya
    RIKEN, Ctr Computat Sci R CCS, Wako, Saitama, Japan.;Univ Tokyo, Grad Sch Informat Sci & Technol, Tokyo, Japan..
    Podobas, Artur
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Beräkningsvetenskap och beräkningsteknik (CST).
    Sano, Kentaro
    RIKEN, Ctr Computat Sci R CCS, Wako, Saitama, Japan..
    Exploration Framework for Synthesizable CGRAs Targeting HPC: Initial Design and Evaluation2022Inngår i: 2022 IEEE 36Th International Parallel And Distributed Processing Symposium Workshops (IPDPSW 2022), Institute of Electrical and Electronics Engineers (IEEE) , 2022, s. 639-646Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Among the more salient accelerator technologies to continue performance scaling in High-Performance Computing (HPC) are Coarse-Grained Reconfigurable Arrays (CGRAs). However, what benefits CGRAs will bring to HPC workloads and how those benefits will be reaped is an open research question today. In this work, we propose a framework to explore the design space of CGRAs for HPC workloads, which includes a tool flow of compilation and simulation, a CGRA HDL library written in SystemVerilog, and a synthesizable CGRA design as a baseline. Using RTL simulation, we evaluate two well-known computation kernels with the baseline CGRA for multiple different architectural parameters. The simulation results demonstrate both correctness and usefulness of our exploration framework.

  • 37.
    Adhi, Boma
    et al.
    Center for Computational Science (R-CCS), RIKEN, Japan.
    Cortes, Carlos
    Center for Computational Science (R-CCS), RIKEN, Japan.
    Ueno, Tomohiro
    Center for Computational Science (R-CCS), RIKEN, Japan.
    Tan, Yiyu
    Iwate University, Department of Systems Innovation Engineering, Japan.
    Kojima, Takuya
    Graduate School of Information Science and Technology, The University of Tokyo, Japan.
    Podobas, Artur
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Beräkningsvetenskap och beräkningsteknik (CST).
    Sano, Kentaro
    Center for Computational Science (R-CCS), RIKEN, Japan.
    Exploring Inter-tile Connectivity for HPC-oriented CGRA with Lower Resource Usage2022Inngår i: FPT 2022: 21st International Conference on Field-Programmable Technology, Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2022Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This research aims to explore the tradeoffs between routing flexibility and hardware resource usage, ultimately reducing the resource usage of our CGRA architecture while maintaining compute efficiency. we investigate statistics of connection usages among switch blocks for benchmark DFGs, propose several CGRA architecture with a reduced connection, and evaluate their hardware cost, routability of DFGs, and computational throughput for benchmarks. We found that the topology with horizontal plus diagonal connection saves about 30% of the resource usage while maintaining virtually the same routing flexibility as the full connectivity topology.

  • 38. Adiban, M.
    et al.
    Safari, A.
    Salvi, Giampiero
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Step-gan: A one-class anomaly detection model with applications to power system security2021Inngår i: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2021, s. 2605-2609Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Smart grid systems (SGSs), and in particular power systems, play a vital role in today's urban life. The security of these grids is now threatened by adversaries that use false data injection (FDI) to produce a breach of availability, integrity, or confidential principles of the system. We propose a novel structure for the multigenerator generative adversarial network (GAN) to address the challenges of detecting adversarial attacks. We modify the GAN objective function and the training procedure for the malicious anomaly detection task. The model only requires normal operation data to be trained, making it cheaper to deploy and robust against unseen attacks. Moreover, the model operates on the raw input data, eliminating the need for feature extraction. We show that the model reduces the well-known mode collapse problem of GAN-based systems, it has low computational complexity and considerably outperforms the baseline system (OCAN) with about 55% in terms of accuracy on a freely available cyber attack dataset.

  • 39. Adiban, Mohammad
    et al.
    Siniscalchi, Marco
    Stefanov, Kalin
    Salvi, Giampiero
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH, Tal-kommunikation. Norwegian University of Science and Technology Trondheim, Norway.
    Hierarchical Residual Learning Based Vector Quantized Variational Autoencorder for Image Reconstruction and Generation2022Inngår i: The 33rd British Machine Vision Conference Proceedings, 2022Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose a multi-layer variational autoencoder method, we call HR-VQVAE, thatlearns hierarchical discrete representations of the data. By utilizing a novel objectivefunction, each layer in HR-VQVAE learns a discrete representation of the residual fromprevious layers through a vector quantized encoder. Furthermore, the representations ateach layer are hierarchically linked to those at previous layers. We evaluate our methodon the tasks of image reconstruction and generation. Experimental results demonstratethat the discrete representations learned by HR-VQVAE enable the decoder to reconstructhigh-quality images with less distortion than the baseline methods, namely VQVAE andVQVAE-2. HR-VQVAE can also generate high-quality and diverse images that outperform state-of-the-art generative models, providing further verification of the efficiency ofthe learned representations. The hierarchical nature of HR-VQVAE i) reduces the decoding search time, making the method particularly suitable for high-load tasks and ii) allowsto increase the codebook size without incurring the codebook collapse problem.

    Fulltekst (pdf)
    fulltext
  • 40.
    Adiban, Mohammad
    et al.
    NTNU, Dept Elect Syst, Trondheim, Norway.;Monash Univ, Dept Human Centred Comp, Melbourne, Australia..
    Siniscalchi, Sabato Marco
    NTNU, Dept Elect Syst, Trondheim, Norway..
    Salvi, Giampiero
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH. NTNU, Dept Elect Syst, Trondheim, Norway..
    A step-by-step training method for multi generator GANs with application to anomaly detection and cybersecurity2023Inngår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 537, s. 296-308Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cyber attacks and anomaly detection are problems where the data is often highly unbalanced towards normal observations. Furthermore, the anomalies observed in real applications may be significantly different from the ones contained in the training data. It is, therefore, desirable to study methods that are able to detect anomalies only based on the distribution of the normal data. To address this problem, we propose a novel objective function for generative adversarial networks (GANs), referred to as STEPGAN. STEP-GAN simulates the distribution of possible anomalies by learning a modified version of the distribution of the task-specific normal data. It leverages multiple generators in a step-by-step interaction with a discriminator in order to capture different modes in the data distribution. The discriminator is optimized to distinguish not only between normal data and anomalies but also between the different generators, thus encouraging each generator to model a different mode in the distribution. This reduces the well-known mode collapse problem in GAN models considerably. We tested our method in the areas of power systems and network traffic control systems (NTCSs) using two publicly available highly imbalanced datasets, ICS (Industrial Control System) security dataset and UNSW-NB15, respectively. In both application domains, STEP-GAN outperforms the state-of-the-art systems as well as the two baseline systems we implemented as a comparison. In order to assess the generality of our model, additional experiments were carried out on seven real-world numerical datasets for anomaly detection in a variety of domains. In all datasets, the number of normal samples is significantly more than that of abnormal samples. Experimental results show that STEP-GAN outperforms several semi-supervised methods while being competitive with supervised methods.

  • 41.
    Adikari, Jithra
    KTH, Skolan för informations- och kommunikationsteknik (ICT), Data- och systemvetenskap, DSV.
    Efficient non-repudiation for techno-information environment2006Inngår i: 2006 International Conference on Industrial and Information Systems, Vols 1 and 2, NEW YORK: IEEE , 2006, s. 454-458Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Non-repudiation means that a person is unable to deny a certain action that he has done under any circumstances. There are several mechanisms, standards and protocols to achieve non-repudiation in techno-information enviromnent. However efficiency in non-repudiation in legal framework was not considerably addressed within the context of those mechanisms. Lack of efficient non-repudiation in the legal framework for techno-information environment makes legal issues when evidence is generated maintained. It can be derived that traditional non-repudiation mechanism delivers efficient non-repudiation. Efficient non-repudiation in techno-information environment is achieved by mapping traditional non-repudiation. Evaluation methodology for efficiency of non-repudiation mechanisms has been improved during this work. Further most significant finding of this research is the Efficient Non-Repudiation Protocol.

  • 42. Adkisson, J. M.
    et al.
    Westlund, Johannes
    KTH.
    Masuhara, H.
    A shell-like model for general purpose programming2019Inngår i: ACM International Conference Proceeding Series, Association for Computing Machinery , 2019Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Shell scripting languages such as bash are designed to integrate with an OS, which mainly involves managing processes with implicit input and output streams. They also attempt to do this in a compact way that could be reasonably typed on a command-line interface. However, existing shell languages are not sufficient to serve as general-purpose languages-values are not observable except in raw streams of bytes, and they lack modern language features such as lexical scope and higher-order functions. By way of a new programming language, Magritte, we propose a general-purpose programming language with semantics similar to bash. In this paper, we discuss the early design of such a system, in which the primary unit of composition, like bash, is processes with input and output channels, which can be read from or written to at any time, and which can be chained together via a pipe operator. We also explore concurrency semantics for such a language.

  • 43.
    Adler, Jonas
    et al.
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.). DeepMind, 6 Pancras Square, London, N1C 4AG, United Kingdom.
    Lunz, Sebastian
    Univ Cambridge, Ctr Math Sci, Cambridge CB3 0WA, England..
    Verdier, Olivier
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.). Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Bergen, Norway.
    Schonlieb, Carola-Bibiane
    Univ Cambridge, Ctr Math Sci, Cambridge CB3 0WA, England..
    Öktem, Ozan
    KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.). Division of Scientific Computing, Department of Information Technology, Uppsala University.
    Task adapted reconstruction for inverse problems2022Inngår i: Inverse Problems, ISSN 0266-5611, E-ISSN 1361-6420, Vol. 38, nr 7, artikkel-id 075006Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The paper considers the problem of performing a post-processing task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem. A key aspect is to formalize the steps of reconstruction and post-processing as appropriate estimators (non-randomized decision rules) in statistical estimation problems. The implementation makes use of (deep) neural networks to provide a differentiable parametrization of the family of estimators for both steps. These networks are combined and jointly trained against suitable supervised training data in order to minimize a joint differentiable loss function, resulting in an end-to-end task adapted reconstruction method. The suggested framework is generic, yet adaptable, with a plug-and-play structure for adjusting both the inverse problem and the post-processing task at hand. More precisely, the data model (forward operator and statistical model of the noise) associated with the inverse problem is exchangeable, e.g., by using neural network architecture given by a learned iterative method. Furthermore, any post-processing that can be encoded as a trainable neural network can be used. The approach is demonstrated on joint tomographic image reconstruction, classification and joint tomographic image reconstruction segmentation.

  • 44.
    Adler, Julien
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Mobile Device Gaze Estimation with Deep Learning: Using Siamese Neural Networks2019Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Gaze tracking has already shown to be a popular technology for desktop devices. When it comes to gaze tracking for mobile devices, however, there is still a lot of progress to be made. There’s still no high accuracy gaze tracking available that works in an unconstrained setting for mobile devices. This work makes contributions in the area of appearance-based unconstrained gaze estimation. Artificial neural networks are trained on GazeCapture, a publicly available dataset for mobile gaze estimation containing over 2 million face images and corresponding gaze labels. In this work, Siamese neural networks are trained to learn linear distances between face images for different gaze points. Then, during inference, calibration points are used to estimate gaze points. This approach is shown to be an effective way of utilizing calibration points in order to improve the result of gaze estimation.

    Fulltekst (pdf)
    fulltext
  • 45.
    Adlers, Jacob
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Pihl, Gustaf
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    Prediction of training time for deep neural networks in TensorFlow2018Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    Machine learning has gained a lot of interest over the past years and is now used extensively in various areas. Google has developed a framework called TensorFlow which simplifies the usage of machine learning without compromising the end result. However, it does not resolve the issue of neural network training being time consuming. The purpose of this thesis is to investigate with what accuracy training times can be predicted using TensorFlow. Essentially, how effectively one neural network in TensorFlow can be used to predict the training times of other neural networks, also in TensorFlow. In order to do this, training times for training different neural networks was collected. This data was used to create a neural network for prediction. The resulting neural network is capable of predicting training times with an average accuracy of 93.017%.

    Fulltekst (pdf)
    bachelor-thesis
  • 46.
    Adolfsson, Fredrik
    KTH, Skolan för elektroteknik och datavetenskap (EECS).
    WebTaint: Dynamic Taint Tracking for Java-based Web Applications2018Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    The internet is a source of information and it connects the world through a single platform. Many businesses have taken advantage of this to share information, to communicate with customers, and to create new business opportunities. However, this does not come without drawbacks as there exists an elevated risk to become targeted in attacks.

    The thesis implemented a dynamic taint tracker, named WebTaint, to detect and prevent confidentiality and integrity vulnerabilities in Java-based web applications. We evaluated to what extent WebTaint can combat integrity vulnerabilities. The possible advantages and disadvantages of using the application is introduced as well as an explication whether the application was capable of being integrated into production services.

    The results show that WebTaint helps to combat SQL Injection and Cross-Site Scripting attacks. However, there are drawbacks in the form of additional time and memory overhead. The implemented solution is therefore not suitable for time or memory sensitive domains. WebTaint could be recommended for use in test environments where security experts utilize the taint tracker to find TaintExceptions through manual and automatic attacks.

    Fulltekst (pdf)
    fulltext
  • 47.
    ADORF, JULIUS
    KTH, Skolan för datavetenskap och kommunikation (CSC).
    Motion Segmentation of RGB-D Videosvia Trajectory Clustering2014Independent thesis Advanced level (degree of Master (Two Years)), 20 poäng / 30 hpOppgave
    Abstract [en]

    Motion segmentation of RGB-D videos can be a first step towards object reconstruction in dynamic scenes. The objective in this thesis is to end an ecient motion segmentation method that can deal with a moving camera. To this end, we adopt a feature-based approach where keypoints in the images are tracked over time. The variation in the observed pairwise 3-d distances is used to determine which of the points move similarly. We then employ spectral clusteringto group trajectories into clusters with similar motion, thereby obtaining a sparse segmentation of the dynamic objectsin the scene. The results on twenty scenes from real world datasets and simulations show that while the method needs more sophistication to segment all of them, several dynamic scenes have been successfully segmented at a processing speed of multiple frames per second.

    Fulltekst (pdf)
    fulltext
  • 48.
    Adriaens, Florian
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Teoretisk datalogi, TCS.
    Gionis, Aristides
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Teoretisk datalogi, TCS.
    Diameter Minimization by Shortcutting with Degree Constraints2022Inngår i: 2022 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM) / [ed] Zhu, X Ranka, S Thai, MT Washio, T Wu, X, Institute of Electrical and Electronics Engineers (IEEE) , 2022, s. 843-848Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We consider the problem of adding a fixed number of new edges to an undirected graph in order to minimize the diameter of the augmented graph, and under the constraint that the number of edges added for each vertex is bounded by an integer. The problem is motivated by network-design applications, where we want to minimize the worst case communication in the network without excessively increasing the degree of any single vertex, so as to avoid additional overload. We present three algorithms for this task, each with their own merits. The special case of a matching augmentation -when every vertex can be incident to at most one new edge- is of particular interest, for which we show an inapproximability result, and provide bounds on the smallest achievable diameter when these edges are added to a path. Finally, we empirically evaluate and compare our algorithms on several real-life networks of varying types.

  • 49.
    Adriaens, Florian
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Teoretisk datalogi, TCS.
    Mara, Alexandru
    IDLab, Ghent University, Belgium.
    Lijffijt, Jefrey
    IDLab, Ghent University, Belgium.
    De Bie, Tijl
    IDLab, Ghent University, Belgium.
    Block-approximated exponential random graphs2020Inngår i: Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020, Institute of Electrical and Electronics Engineers Inc. , 2020, s. 70-80Konferansepaper (Fagfellevurdert)
    Abstract [en]

    An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs. By utilizing fast matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions, while being able to meaningfully model local information of the graph (e.g., degrees) as well as global information (e.g., clustering coefficient, assortativity, etc.) if desired. This allows one to efficiently generate random networks with similar properties as an observed network, and the models can be used for several downstream tasks such as link prediction. Our methods are scalable to sparse graphs consisting of millions of nodes.Empirical evaluation demonstrates competitiveness in terms of both speed and accuracy with state-of-the-art methods - which are typically based on embedding the graph into some low-dimensional space - for link prediction, showcasing the potential of a more direct and interpretable probablistic model for this task.

  • 50.
    Adriaens, Florian
    et al.
    University of Helsinki, Helsinki, Finland.
    Wang, Hongliang
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Teoretisk datalogi, TCS.
    Gionis, Aristides
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Datavetenskap, Teoretisk datalogi, TCS.
    Minimizing hitting time between disparate groups with shortcut edges2023Inngår i: KDD 2023: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery (ACM) , 2023, s. 1-10Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Structural bias or segregation of networks refers to situations where two or more disparate groups are present in the network, so that the groups are highly connected internally, but loosely connected to each other. Examples include polarized communities in social networks, antagonistic content in video-sharing or news-feed platforms, etc. In many cases it is of interest to increase the connectivity of disparate groups so as to, e.g., minimize social friction, or expose individuals to diverse viewpoints. A commonly-used mechanism for increasing the network connectivity is to add edge shortcuts between pairs of nodes. In many applications of interest, edge shortcuts typically translate to recommendations, e.g., what video to watch, or what news article to read next. The problem of reducing structural bias or segregation via edge shortcuts has recently been studied in the literature, and random walks have been an essential tool for modeling navigation and connectivity in the underlying networks. Existing methods, however, either do not offer approximation guarantees, or engineer the objective so that it satisfies certain desirable properties that simplify the optimization task. In this paper we address the problem of adding a given number of shortcut edges in the network so as to directly minimize the average hitting time and the maximum hitting time between two disparate groups. The objectives we study are more natural than objectives considered earlier in the literature (e.g., maximizing hitting-time reduction) and the optimization task is significantly more challenging. Our algorithm for minimizing average hitting time is a greedy bicriteria that relies on supermodularity. In contrast, maximum hitting time is not supermodular. Despite, we develop an approximation algorithm for that objective as well, by leveraging connections with average hitting time and the asymmetric k-center problem.

1234567 1 - 50 of 6427
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf