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  • 1.
    Abbas, Zainab
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Al-Shishtawy, Ahmad
    RISE SICS, Stockholm, Sweden.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. RISE SICS, Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks2018Conference paper (Refereed)
    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.

  • 2.
    Abbas, Zainab
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Kalavri, Vasiliki
    Systems Group, ETH, Zurich, Switzerland.
    Carbone, Paris
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Streaming Graph Partitioning: An Experimental Study2018In: Proceedings of the VLDB Endowment, ISSN 2150-8097, E-ISSN 2150-8097, Vol. 11, no 11, p. 1590-1603Article in journal (Refereed)
    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.

  • 3.
    Apolonia, Nuno
    et al.
    Universitat Politecnica de Catalunya (UPC) Barcelona, Spain.
    Antaris, Stefanos
    Girdzijauskas, Šarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Pallis, G.
    Dikaiakos, Marios
    SELECT: A distributed publish/subscribe notification system for online social networks2018In: Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 970-979, article id 8425250Conference paper (Refereed)
    Abstract [en]

    Publish/subscribe (pub/sub) mechanisms constitutean attractive communication paradigm in the design of large-scale notification systems for Online Social Networks (OSNs). Toaccommodate the large-scale workloads of notifications producedby OSNs, pub/sub mechanisms require thousands of serversdistributed on different data centers all over the world, incurringlarge overheads. To eliminate the pub/sub resources used, wepropose SELECT - a distributed pub/sub social notificationsystem over peer-to-peer (P2P) networks. SELECT organizesthe peers on a ring topology and provides an adaptive P2Pconnection establishment algorithm where each peer identifiesthe number of connections required, based on the social structureand user availability. This allows to propagate messages to thesocial friends of the users using a reduced number of hops.The presented algorithm is an efficient heuristic to an NP-hard problem which maps workload graphs to structured P2Poverlays inducing overall, close to theoretical, minimal number ofmessages. Experiments show that SELECT reduces the numberof relay nodes up to 89% versus the state-of-the-art pub/subnotification systems. Additionally, we demonstrate the advantageof SELECT against socially-aware P2P overlay networks andshow that the communication between two socially connectedpeers is reduced on average by at least 64% hops, while achieving100% communication availability even under high churn.

  • 4.
    Bahri, Leila
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Carminati, Barbara
    Ferrari, Elena
    Univ Insubria, Dept Theoret & Appl Sci, Varese, Italy..
    Knowledge-based approaches for identity management in online social networks2018In: WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, ISSN 1942-4787, Vol. 8, no 5, article id e1260Article, review/survey (Refereed)
    Abstract [en]

    When we meet a new person, we start by introducing ourselves. We share our names, and other information about our jobs, cities, family status, and so on. This is how socializing and social interactions can start: we first need to identify each other. Identification is a cornerstone in establishing social contacts. We identify ourselves and others by a set of civil (e.g., name, nationality, ID number, gender) and social (e.g., music taste, hobbies, religion) characteristics. This seamlessly carried out identification process in face-to-face interactions is challenged in the virtual realms of socializing, such as in online social network (OSN) platforms. New identities (i.e., online profiles) could be created without being subject to any level of verification, making it easy to create fake information and forge fake identities. This has led to a massive proliferation of accounts that represent fake identities (i.e., not mapping to physically existing entities), and that poison the online socializing environment with fake information and malicious behavior (e.g., child abuse, information stealing). Within this milieu, users in OSNs are left unarmed against the challenging task of identifying the real person behind the screen. OSN providers and research bodies have dedicated considerable effort to the study of the behavior and features of fake OSN identities, trying to find ways to detect them. Some other research initiatives have explored possible techniques to enable identity validation in OSNs. Both kinds of approach rely on extracting knowledge from the OSN, and exploiting it to achieve identification management in their realms. We provide a review of the most prominent works in the literature. We define the problem, provide a taxonomy of related attacks, and discuss the available solutions and approaches for knowledge-based identity management in OSNs. This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Application Areas> Internet and Web-Based Applications Application Areas> Society and Culture

  • 5.
    Bahri, Leila
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    When Trust Saves Enegry - A Reference Franework for Proof-of-Trust (PoT) Blockchains2018In: WWW '18 Companion Proceedings of the The Web Conference 2018, ACM Digital Library, 2018, p. 1165-1169Conference paper (Refereed)
    Abstract [en]

    Blockchains are attracting the attention of many technical, financial, and industrial parties, as a promising infrastructure for achieving secure peer-to-peer (P2P) transactional systems. At the heart of blockchains is proof-of-work (PoW), a trustless leader election mechanism based on demonstration of computational power. PoW provides blockchain security in trusless P2P environments, but comes at the expense of wasting huge amounts of energy. In this research work, we question this energy expenditure of PoW under blockchain use cases where some form of trust exists between the peers. We propose a Proof-of-Trust (PoT) blockchain where peer trust is valuated in the network based on a trust graph that emerges in a decentralized fashion and that is encoded in and managed by the blockchain itself. This trust is then used as a waiver for the difficulty of PoW; that is, the more trust you prove in the network, the less work you do.

  • 6.
    Borlenghi, Simone
    et al.
    KTH, School of Engineering Sciences (SCI), Applied Physics, Materials and Nanophysics. KTH Royal Inst Technol, Sch Engn Sci, Dept Appl Phys, Electrum 229, SE-16440 Kista, Sweden..
    Boman, Magnus
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. KTH Royal Inst Technol, EECS SCS, Electrum 229, SE-16440 Kista, Sweden.;RISE SICS, Electrum 229, SE-16429 Kista, Sweden..
    Delin, Anna
    KTH, School of Industrial Engineering and Management (ITM), Materials Science and Engineering, Applied Material Physics. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH Royal Inst Technol, Sch Engn Sci, Dept Appl Phys, Electrum 229, SE-16440 Kista, Sweden.;KTH Royal Inst Technol, SeRC, SE-10044 Stockholm, Sweden..
    Modeling reservoir computing with the discrete nonlinear Schrodinger equation2018In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 98, no 5, article id 052101Article in journal (Refereed)
    Abstract [en]

    We formulate, using the discrete nonlinear Schrodinger equation (DNLS), a general approach to encode and process information based on reservoir computing. Reservoir computing is a promising avenue for realizing neuromorphic computing devices. In such computing systems, training is performed only at the output level by adjusting the output from the reservoir with respect to a target signal. In our formulation, the reservoir can be an arbitrary physical system, driven out of thermal equilibrium by an external driving. The DNLS is a general oscillator model with broad application in physics, and we argue that our approach is completely general and does not depend on the physical realization of the reservoir. The driving, which encodes the object to be recognized, acts as a thermodynamic force, one for each node in the reservoir. Currents associated with these thermodynamic forces in turn encode the output signal from the reservoir. As an example, we consider numerically the problem of supervised learning for pattern recognition, using as a reservoir a network of nonlinear oscillators.

  • 7. Bousse, Erwan
    et al.
    Leroy, Dorian
    Combemale, Benoit
    Wimmer, Manuel
    Baudry, Benoit
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Omniscient debugging for executable DSLs2018In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 137, p. 261-288Article in journal (Refereed)
    Abstract [en]

    Omniscient debugging is a promising technique that relies on execution traces to enable free traversal of the states reached by a model (or program) during an execution. While a few General-Purpose Languages (GPLs) already have support for omniscient debugging, developing such a complex tool for any executable Domain Specific Language (DSL) remains a challenging and error prone task. A generic solution must: support a wide range of executable DSLs independently of the metaprogramming approaches used for implementing their semantics; be efficient for good responsiveness. Our contribution relies on a generic omniscient debugger supported by efficient generic trace management facilities. To support a wide range of executable DSLs, the debugger provides a common set of debugging facilities, and is based on a pattern to define runtime services independently of metaprogramming approaches. Results show that our debugger can be used with various executable DSLs implemented with different metaprogramming approaches. As compared to a solution that copies the model at each step, it is on average sixtimes more efficient in memory, and at least 2.2 faster when exploring past execution states, while only slowing down the execution 1.6 times on average.

  • 8.
    Broman, David
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Hybrid Simulation Safety: Limbos and Zero Crossings2018In: Principles of Modeling: Essays Dedicated to Edward A. Lee on the Occasion of His 60th Birthday, Springer, 2018, p. 106-121Chapter in book (Refereed)
    Abstract [en]

    Physical systems can be naturally modeled by combining continuous and discrete models. Such hybrid models may simplify the modeling task of complex system, as well as increase simulation performance. Moreover, modern simulation engines can often efficiently generate simulation traces, but how do we know that the simulation results are correct? If we detect an error, is the error in the model or in the simulation itself? This paper discusses the problem of simulation safety, with the focus on hybrid modeling and simulation. In particular, two key aspects are studied: safe zero-crossing detection and deterministic hybrid event handling. The problems and solutions are discussed and partially implemented in Modelica and Ptolemy II.

  • 9.
    Broman, David
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Siek, J. G.
    United States.
    Gradually typed symbolic expressions2017In: PEPM 2018 - Proceedings of the ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation, Co-located with POPL 2018, Association for Computing Machinery (ACM), 2017, p. 15-29Conference paper (Refereed)
    Abstract [en]

    Embedding a domain-specific language (DSL) in a general purpose host language is an efficient way to develop a new DSL. Various kinds of languages and paradigms can be used as host languages, including object-oriented, functional, statically typed, and dynamically typed variants, all having their pros and cons. For deep embedding, statically typed languages enable early checking and potentially good DSL error messages, instead of reporting runtime errors. Dynamically typed languages, on the other hand, enable flexible transformations, thus avoiding extensive boilerplate code. In this paper, we introduce the concept of gradually typed symbolic expressions that mix static and dynamic typing for symbolic data. The key idea is to combine the strengths of dynamic and static typing in the context of deep embedding of DSLs. We define a gradually typed calculus <*>, formalize its type system and dynamic semantics, and prove type safety. We introduce a host language called Modelyze that is based on <*>, and evaluate the approach by embedding a series of equation-based domain-specific modeling languages, all within the domain of physical modeling and simulation.

  • 10.
    Carbone, Paris
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Scalable and Reliable Data Stream Processing2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Data-stream management systems have for long been considered as a promising architecture for fast data management. The stream processing paradigm poses an attractive means of declaring persistent application logic coupled with state over evolving data. However, despite contributions in programming semantics addressing certain aspects of data streaming, existing approaches have been lacking a clear, universal specification for the underlying system execution. We investigate the case of data stream processing as a general-purpose scalable computing architecture that can support continuous and iterative state-driven workloads. Furthermore, we examine how this architecture can enable the composition of reliable, reconfigurable services and complex applications that go even beyond the needs of scalable data analytics, a major trend in the past decade.

    In this dissertation, we specify a set of core components and mechanisms to compose reliable data stream processing systems while adopting three crucial design principles: blocking-coordination avoidance, programming-model transparency, and compositionality. Furthermore, we identify the core open challenges among the academic and industrial state of the art and provide a complete solution using these design principles as a guide. Our contributions address the following problems: I) Reliable Execution and Stream State Management, II) Computation Sharing and Semantics for Stream Windows, and III) Iterative Data Streaming. Several parts of this work have been integrated into Apache Flink, a widely-used, open-source scalable computing framework, and supported the deployment of hundreds of long-running large-scale production pipelines worldwide.

  • 11.
    Castañeda Lozano, Roberto
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. RISE SICS (Swedish Institute of Computer Science).
    Constraint-Based Register Allocation and Instruction Scheduling2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to improve latency or throughput) are central compiler problems. This dissertation proposes a combinatorial optimization approach to these problems that delivers optimal solutions according to a model, captures trade-offs between conflicting decisions, accommodates processor-specific features, and handles different optimization criteria.

    The use of constraint programming and a novel program representation enables a compact model of register allocation and instruction scheduling. The model captures the complete set of global register allocation subproblems (spilling, assignment, live range splitting, coalescing, load-store optimization, multi-allocation, register packing, and rematerialization) as well as additional subproblems that handle processor-specific features beyond the usual scope of conventional compilers.

    The approach is implemented in Unison, an open-source tool used in industry and research that complements the state-of-the-art LLVM compiler. Unison applies general and problem-specific constraint solving methods to scale to medium-sized functions, solving functions of up to 647 instructions optimally and improving functions of up to 874 instructions. The approach is evaluated experimentally using different processors (Hexagon, ARM and MIPS), benchmark suites (MediaBench and SPEC CPU2006), and optimization criteria (speed and code size reduction). The results show that Unison generates code of slightly to significantly better quality than LLVM, depending on the characteristics of the targeted processor (1% to 9.3% mean estimated speedup; 0.8% to 3.9% mean code size reduction). Additional experiments for Hexagon show that its estimated speedup has a strong monotonic relationship to the actual execution speedup, resulting in a mean speedup of 5.4% across MediaBench applications.

    The approach contributed by this dissertation is the first of its kind that is practical (it captures the complete set of subproblems, scales to medium-sized functions, and generates executable code) and effective (it generates better code than the LLVM compiler, fulfilling the promise of combinatorial optimization). It can be applied to trade compilation time for code quality beyond the usual optimization levels, explore and exploit processor-specific features, and identify improvement opportunities in conventional compilers.

  • 12.
    Castañeda Lozano, Roberto
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. RISE SICS (Swedish Institute of Computer Science).
    Carlsson, Mats
    RISE SICS (Swedish Institute of Computer Science).
    Hjort Blindell, Gabriel
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Schulte, Christian
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Combinatorial Register Allocation and Instruction Scheduling2018Report (Other academic)
    Abstract [en]

    This paper introduces a combinatorial optimization approach to register allocation and instruction scheduling, two central compiler problems. Combinatorial optimization has the potential to solve these problems optimally and to exploit processor-specific features readily. Our approach is the first to leverage this potential in practice: it captures the complete set of program transformations used in state-of-the-art compilers, scales to medium-sized functions of up to 1000 instructions, and generates executable code. This level of practicality is reached by using constraint programming, a particularly suitable combinatorial optimization technique. Unison, the implementation of our approach, is open source, used in industry, and integrated with the LLVM toolchain.

    An extensive evaluation of estimated speed, code size, and scalability confirms that Unison generates better code than LLVM while scaling to medium-sized functions. The evaluation uses systematically selected benchmarks from MediaBench and SPEC CPU2006 and different processor architectures (Hexagon, ARM, MIPS). Mean estimated speedup ranges from 1% to 9.3% and mean code size reduction ranges from 0.8% to 3.9% for the different architectures. Executing the generated code on Hexagon confirms that the estimated speedup indeed results in actual speedup. Given a fixed time limit, Unison solves optimally functions of up to 647 instructions, delivers improved solutions for functions of up to 874 instructions, and achieves more than 85% of the potential speed for 90% of the functions on Hexagon.

    The results in this paper show that our combinatorial approach can be used in practice to trade compilation time for code quality beyond the usual compiler optimization levels, fully exploit processor-specific features, and identify improvement opportunities in existing heuristic algorithms.

  • 13.
    Castañeda Lozano, Roberto
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. RISE SICS (Swedish Institute of Computer Science).
    Schulte, Christian
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Survey on Combinatorial Register Allocation and Instruction Scheduling2018In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341Article in journal (Refereed)
    Abstract [en]

    Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time.

    This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization.

  • 14.
    Corcoran, Diarmuid
    et al.
    KTH. Ericsson AB.
    Andimeh, Loghman
    Ericsson AB.
    Ermedahl, Andreas
    Ericsson AB.
    Kreuger, Per
    RISE SICS AB.
    Schulte, Christian
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Data Driven Selection of DRX for Energy Efficient 5G RAN2017In: 2017 13TH INTERNATIONAL CONFERENCE ON NETWORK AND SERVICE MANAGEMENT (CNSM), IEEE , 2017Conference paper (Refereed)
    Abstract [en]

    The number of connected mobile devices is increasing rapidly with more than 10 billion expected by 2022. Their total aggregate energy consumption poses a significant concern to society. The current 3gpp (3rd Generation Partnership Project) LTE/LTE-Advanced standard incorporates an energy saving technique called discontinuous reception (DRX). It is expected that 5G will use an evolved variant of this scheme. In general, the single selection of DRX parameters per device is non trivial. This paper describes how to improve energy efficiency of mobile devices by selecting DRX based on the traffic profile per device. Our particular approach uses a two phase data-driven strategy which tunes the selection of DRX parameters based on a smart fast energy model. The first phase involves the off-line selection of viable DRX combinations for a particular traffic mix. The second phase involves an on-line selection of DRX from this viable list. The method attempts to guarantee that latency is not worse than a chosen threshold. Alternatively, longer battery life for a device can be traded against increased latency. We built a lab prototype of the system to verify that the technique works and scales on a real LTE system. We also designed a sophisticated traffic generator based on actual user data traces. Complementary method verification has been made by exhaustive off-line simulations on recorded LTE network data. Our approach shows significant device energy savings, which has the aggregated potential over billions of devices to make a real contribution to green, energy efficient networks.

  • 15.
    Durieux, Thomas
    et al.
    Univ Lille, Lille, France.;INRIA, Le Chesnay, France..
    Hamadi, Youssef
    Ecole Polytech, Palaiseau, France..
    Yu, Zhongxing
    Univ Lille, Lille, France.;INRIA, Le Chesnay, France..
    Baudry, Benoit
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Monperrus, Martin
    KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.
    Exhaustive Exploration of the Failure-oblivious Computing Search Space2018In: 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VERIFICATION AND VALIDATION (ICST), IEEE Press, 2018, p. 139-149Conference paper (Refereed)
    Abstract [en]

    High-availability of software systems requires automated handling of crashes in presence of errors. Failure-oblivious computing is one technique that aims to achieve high availability. We note that failure-obliviousness has not been studied in depth yet, and there is very few study that helps understand why failure-oblivious techniques work. In order to make failure-oblivious computing to have an impact in practice, we need to deeply understand failure-oblivious behaviors in software. In this paper, we study, design and perform an experiment that analyzes the size and the diversity of the failure-oblivious behaviors. Our experiment consists of exhaustively computing the search space of 16 field failures of large-scale open-source Java software. The outcome of this experiment is a much better understanding of what really happens when failure-oblivious computing is used, and this opens new promising research directions.

  • 16.
    Edman, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Sequential Pattern Mining on Electronic Medical Records for Finding Optimal Clinical Pathways2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Electronic Medical Records (EMRs) are digital versions of paper charts, used to record the treatment of different patients in hospitals. Clinical pathways are used as guidelines for how to treat different diseases, determined by observing outcomes from previous treatments. Sequential pattern mining is a version of data mining where the data mined is organized in sequences. It is a common research topic in data mining with many new variations on existing algorithms being introduced frequently. In a previous report, the sequential pattern mining algorithm PrefixSpan was used to mine patterns in EMRs to verify or suggest new clinical pathways. It was found to only be able to verify pathways partially. One of the reasons stated for this was that PrefixSpan was too inefficient to be able to mine at a low enough support to consider some items. In this report CSpan is used instead, since it is supposed to outperform PrefixSpan by up to two orders of magnitude, in order to improve runtime and thereby address the problems mentioned in the previous work. The results show that CSpan did indeed improve the runtime and the algorithm was able to mine at a lower minimum support. However, the output was only barely improved.

  • 17.
    Engelin, Martin
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    CapsNet Comprehension of Objects in Different Rotational Views: A comparative study of capsule and convolutional networks2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Capsule network (CapsNet) is a new and promising approach to computer vision. In the small amount of research published so far, it has shown to be good at generalizing complex objects and perform well even when the images are skewed or the objects are seen from unfamiliar viewpoints. This thesis further tests this ability of CapsNetby comparing it to convolutional networks (ConvNets) on the task to understand images of clothing in different rotational views. Even though the ConvNets have a higher classification accuracy than CapsNets, the results indicate that CapsNets are better at understanding the clothes when viewed in different rotational views.

  • 18. Gomes, Claudio
    et al.
    Thule, Casper
    Broman, David
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Larsen, Peter Gorm
    Vangheluwe, Hans
    Co-Simulation: A Survey2018In: ACM Computing Surveys, ISSN 0360-0300, E-ISSN 1557-7341, Vol. 51, no 3, article id 49Article in journal (Refereed)
    Abstract [en]

    Modeling and simulation techniques are today extensively used both in industry and science. Parts of larger systems are, however, typically modeled and simulated by different techniques, tools, and algorithms. In addition, experts from different disciplines use various modeling and simulation techniques. Both these facts make it difficult to study coupled heterogeneous systems. Co-simulation is an emerging enabling technique, where global simulation of a coupled system can be achieved by composing the simulations of its parts. Due to its potential and interdisciplinary nature, cosimulation is being studied in different disciplines but with limited sharing of findings. In this survey, we study and survey the state-of-the-art techniques for co-simulation, with the goal of enhancing future research and highlighting the main challenges. To study this broad topic, we start by focusing on discrete-event-based co-simulation, followed by continuous-time-based co-simulation. Finally, we explore the interactions between these two paradigms, in hybrid co-simulation. To survey the current techniques, tools, and research challenges, we systematically classify recently published research literature on co-simulation, and summarize it into a taxonomy. As a result, we identify the need for finding generic approaches for modular, stable, and accurate coupling of simulation units, as well as expressing the adaptations required to ensure that the coupling is correct.

  • 19.
    Hammar, Kim
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Deep Text Mining of Instagram Data Without Strong Supervision2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. This data can be analyzed for the purpose of improving user recommendations and detecting trends. The grand volume of unstructured text that is available makes the intersection of text processing and machine learning a promising avenue of research. Current methods that use machine learning for text processing are in many cases dependent on annotated training data. However, considering the heterogeneity and variability of social media, obtaining strong supervision for social media data is in practice both difficult and expensive. In light of this limitation, a belief that has put its marks on this thesis is that the study of text mining methods that can be applied without strong supervision is of a higher practical interest.

    This thesis investigates unsupervised methods for scalable processing of text from social media. Particularly, the thesis targets a classification and extraction task in the fashion domain on the image-sharing platform Instagram. Instagram is one of the largest social media platforms, containing both text and images. Still, research on text processing in social media is to a large extent limited to Twitter data, and little attention has been paid to text mining of Instagram data. The aim of this thesis is to broaden the scope of state-of-the-art methods for information extraction and text classification to the unsupervised setting, working with informal text on Instagram. Its main contributions are (1) an empirical study of text from Instagram; (2) an evaluation of word embeddings for Instagram text; (3) a distributed implementation of the FastText algorithm; (4) a system for fashion attribute extraction in Instagram using word embeddings; and (5) a multi-label clothing classifier for Instagram text, built with deep learning techniques and minimal supervision.

    The empirical study demonstrates that the text distribution on Instagram exhibits the long-tail phenomenon, that the text is just as noisy as have been reported in studies on Twitter text, and that comment sections are multi-lingual. In experiments with word embeddings for Instagram, the importance of hyperparameter tuning is manifested and a mismatch between pre-trained embeddings and social media is observed. Furthermore, that word embeddings are a useful asset for information extraction is confirmed. Experimental results show that word embeddings beats a baseline that uses Levenshtein distance on the task of extracting fashion attributes from Instagram. The results also show that the distributed implementation of FastText reduces the time it takes to train word embeddings with a factor that scales with the number of machines used for training. Finally, our research demonstrates that weak supervision can be used to train a deep classifier, achieving an F1 score of 0.61 on the task of classifying clothes in Instagram posts based only on the associated text, which is on par with human performance.

  • 20. Ingmar, Linnea
    et al.
    Schulte, Christian
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Making Compact-Table Compact2018In: 24th International Conference on the Principles and Practice of Constraint Programming, CP 2018, Springer, 2018, Vol. 11008, p. 210-218Conference paper (Refereed)
    Abstract [en]

    The compact-table propagator for table constraints appears to be a strong candidate for inclusion into any constraint solver due to its efficiency and simplicity. However, successful integration into a constraint solver based on copying rather than trailing is not obvious: while the underlying bit-set data structure is sparse for efficiency it is not compact for memory, which is essential for a copying solver. The paper introduces techniques to make compact-table an excellent fit for a copying solver. The key is to make sparse bit-sets dynamically compact (only their essential parts occupy memory and their implementation is dynamically adapted during search) and tables shared (their read-only parts are shared among copies). Dynamically compact bit-sets reduce peak memory by 7.2% and runtime by 13.6% on average and by up to 66.3% and 33.2%. Shared tables even further reduce runtime and memory usage. The reduction in runtime exceeds the reduction in memory and a cache analysis indicates that our techniques might also be beneficial for trailing solvers. The proposed implementation has replaced Gecode’s original implementations as it runs on average almost an order of magnitude faster while using half the memory.

  • 21.
    Issa, Shady
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. INESC-ID, Instituto Superior Tecnico, Universidade de Lisboa.
    Techniques for Enhancing the Efficiency of Transactional Memory Systems2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Transactional Memory (TM) is an emerging programming paradigm that drastically simplifies the development of concurrent applications by relieving programmers from a major source of complexity: how to ensure correct, yet efficient, synchronization of concurrent accesses to shared memory. Despite the large body of research devoted to this area, existing TM systems still suffer from severe limitations that hamper both their performance and energy efficiency.

    This dissertation tackles the problem of how to build efficient implementations of the TM abstraction by introducing innovative techniques that address three crucial limitations of existing TM systems by: (i) extending the effective capacity of Hardware TM (HTM) implementations; (ii) reducing the synchronization overheads in Hybrid TM (HyTM) systems; (iii) enhancing the efficiency of TM applications via energy-aware contention management schemes.

    The first contribution of this dissertation, named POWER8-TM (P8TM), addresses what is arguably one of the most compelling limitations of existing HTM implementations: the inability to process transactions whose footprint exceeds the capacity of the processor's cache. By leveraging, in an innovative way, two hardware features provided by IBM POWER8 processors, namely Rollback-only Transactions and Suspend/Resume, P8TM can achieve up to 7x performance gains in workloads that stress the capacity limitations of HTM.

    The second contribution is Dynamic Memory Partitioning-TM (DMP-TM), a novel Hybrid TM (HyTM) that offloads the cost of detecting conflicts between HTM and Software TM (STM) to off-the-shelf operating system memory protection mechanisms. DMP-TM's design is agnostic to the STM algorithm and has the key advantage of allowing for integrating, in an efficient way, highly scalable STM implementations that would, otherwise, demand expensive instrumentation of the HTM path. This allows DMP-TM to achieve up to 20x speedups compared to state of the art HyTM solutions in uncontended workloads.

    The third contribution, Green-CM, is an energy-aware Contention Manager (CM) that has two main innovative aspects: (i) a novel asymmetric design, which combines different back-off policies in order to take advantage of Dynamic Frequency and Voltage Scaling (DVFS) hardware capabilities, available in most modern processors; (ii) an energy efficient implementation of a fundamental building block for many CM implementations, namely, the mechanism used to back-off threads for a predefined amount of time. Thanks to its innovative design, Green-CM can reduce the Energy Delay Product by up to 2.35x with respect to state of the art CMs.

    All the techniques proposed in this dissertation share an important common feature that is essential to preserve the ease of use of the TM abstraction: the reliance on on-line self-tuning mechanisms that ensure robust performance even in presence of heterogeneous workloads, without requiring prior knowledge of the target workloads or architecture.

  • 22. Johansson, U.
    et al.
    Linusson, H.
    Löfström, T.
    Boström, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Interpretable regression trees using conformal prediction2018In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 97, p. 394-404Article in journal (Refereed)
    Abstract [en]

    A key property of conformal predictors is that they are valid, i.e., their error rate on novel data is bounded by a preset level of confidence. For regression, this is achieved by turning the point predictions of the underlying model into prediction intervals. Thus, the most important performance metric for evaluating conformal regressors is not the error rate, but the size of the prediction intervals, where models generating smaller (more informative) intervals are said to be more efficient. State-of-the-art conformal regressors typically utilize two separate predictive models: the underlying model providing the center point of each prediction interval, and a normalization model used to scale each prediction interval according to the estimated level of difficulty for each test instance. When using a regression tree as the underlying model, this approach may cause test instances falling into a specific leaf to receive different prediction intervals. This clearly deteriorates the interpretability of a conformal regression tree compared to a standard regression tree, since the path from the root to a leaf can no longer be translated into a rule explaining all predictions in that leaf. In fact, the model cannot even be interpreted on its own, i.e., without reference to the corresponding normalization model. Current practice effectively presents two options for constructing conformal regression trees: to employ a (global) normalization model, and thereby sacrifice interpretability; or to avoid normalization, and thereby sacrifice both efficiency and individualized predictions. In this paper, two additional approaches are considered, both employing local normalization: the first approach estimates the difficulty by the standard deviation of the target values in each leaf, while the second approach employs Mondrian conformal prediction, which results in regression trees where each rule (path from root node to leaf node) is independently valid. An empirical evaluation shows that the first approach is as efficient as current state-of-the-art approaches, thus eliminating the efficiency vs. interpretability trade-off present in existing methods. Moreover, it is shown that if a validity guarantee is required for each single rule, as provided by the Mondrian approach, a penalty with respect to efficiency has to be paid, but it is only substantial at very high confidence levels.

  • 23. Kalavri, Vasiliki
    et al.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Haridi, Seif
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    High-Level Programming Abstractions for Distributed Graph Processing2018In: IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, E-ISSN 1558-2191, Vol. 30, no 2, p. 305-324Article in journal (Refereed)
    Abstract [en]

    Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled as graphs appear in application domains such as machine learning, recommendation, web search, and social network analysis. Writing distributed graph applications is inherently hard and requires programming models that can cover a diverse set of problems, including iterative refinement algorithms, graph transformations, graph aggregations, pattern matching, ego-network analysis, and graph traversals. Several high-level programming abstractions have been proposed and adopted by distributed graph processing systems and big data platforms. Even though significant work has been done to experimentally compare distributed graph processing frameworks, no qualitative study and comparison of graph programming abstractions has been conducted yet. In this survey, we review and analyze the most prevalent high-level programming models for distributed graph processing, in terms of their semantics and applicability. We review 34 distributed graph processing systems with respect to the graph processing models they implement and we survey applications that appear in recent distributed graph systems papers. Finally, we discuss trends and open research questions in the area of distributed graph processing.

  • 24.
    Karlsson, Vide
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Utvärdering av nyckelordsbaserad textkategoriseringsalgoritmer2016Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Supervised learning algorithms have been used for automatic text categoriza- tion with very good results. But supervised learning requires a large amount of manually labeled training data and this is a serious limitation for many practical applications. Keyword-based text categorization does not require manually la- beled training data and has therefore been presented as an attractive alternative to supervised learning. The aim of this study is to explore if there are other li- mitations for using keyword-based text categorization in industrial applications. This study also tests if a new lexical resource, based on the paradigmatic rela- tions between words, could be used to improve existing keyword-based text ca- tegorization algorithms. An industry motivated use case was created to measure practical applicability. The results showed that none of five examined algorithms was able to meet the requirements in the industrial motivated use case. But it was possible to modify one algorithm proposed by Liebeskind et.al. (2015) to meet the requirements. The new lexical resource produced relevant keywords for text categorization but there was still a large variance in the algorithm’s capaci- ty to correctly categorize different text categories. The categorization capacity was also generally too low to meet the requirements in many practical applica- tions. Further studies are needed to explore how the algorithm’s categorization capacity could be improved. 

  • 25.
    Kefato, Zekarias
    et al.
    Trento Univesrity.
    Sheikh, Nasrullah
    Trento University.
    Bahri, Leila
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Soliman, Amira
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Montresor, Alberto
    Trento University.
    CAS2VEC: Network-Agnostic Cascade Prediction in Online Social Networks2018In: The 5th International Symposium on Social Networks Analysis, Management and Security (SNAMS-2018), IEEE, 2018Conference paper (Refereed)
  • 26.
    Kefato, Zekarias
    et al.
    Trento University.
    Sheikh, Nasrullah
    Trento University.
    Bahri, Leila
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Soliman, Amira
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Montresor, Alberto
    Trento University.
    CaTS: Network-Agnostic Virality Prediction Model to Aid Rumour Detection2018Conference paper (Refereed)
  • 27.
    Khan, Amin M.
    et al.
    Department of Computer Science, UiT The Arctic University of Norway. Tromsø, Norway.
    Freitag, Felix
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain .
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Ha, Phuong Hoai
    Department of Computer Science, UiT The Arctic University of Norway. Tromsø, Norway.
    Demo Abstract: Towards IoT Service Deployments on Edge Community Network Microclouds2018Conference paper (Refereed)
    Abstract [en]

    Internet of Things (IoT) services for personal devices and smart homes provided by commercial solutions are typically proprietary and closed. These services provide little control to the end users, for instance to take ownership of their data and enabling services, which hinders these solutions' wider acceptance. In this demo paper, we argue for an approach to deploy professional IoT services on user-controlled infrastructure at the network edge. The users would benefit from the ability to choose the most suitable service from different IoT service offerings, like the one which satisfies their privacy requirements, and third-party service providers could offer more tailored IoT services at customer premises. We conduct the demonstration on microclouds, which have been built with the Cloudy platform in the Guifi.net community network. The demonstration is conducted from the perspective of end users, who wish to deploy professional IoT data management and analytics services in volunteer microclouds.

  • 28. Kolbay, B.
    et al.
    Mrazovic, Petar
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Larriba-Pey, J. L.
    Analyzing last mile delivery operations in barcelona’s urban freight transport network2018In: 2nd EAI International Conference on ICT Infrastructures and Services for Smart Cities, IISSC 2017 and 2nd International Conference on Cloud, Networking for IoT systems, CN4IoT 2017, Springer Verlag , 2018, p. 13-22Conference paper (Refereed)
    Abstract [en]

    Barcelona has recently started a new strategy to control and understand Last Mile Delivery, AreaDUM. The strategy is to provide freight delivery vehicle drivers with a mobile app that has to be used every time their vehicle is parked in one of the designated AreaDUM surface parking spaces in the streets of the city. This provides a significant amount of data about the activity of the freight delivery vehicles, their patterns, the occupancy of the spaces, etc. In this paper, we provide a preliminary set of analytics preceded by the procedures employed for the cleansing of the dataset. During the analysis we show that some data blur the results and using a simple strategy to detect when a vehicle parks repeatedly in close-by parking slots, we are able to obtain different, yet more reliable results. In our paper, we show that this behavior is common among users with 80\% prevalence. We conclude that we need to analyse and understand the user behaviors further with the purpose of providing predictive algorithms to find parking lots and smart routing algorithms to minimize traffic.

  • 29.
    Linusson, Henrik
    et al.
    Department of Information Technology, University of Borås, Sweden.
    Norinder, Ulf
    Swetox, Karolinska Institutet, Unit of Toxicology Sciences, Sweden.
    Boström, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Johansson, Ulf
    Högskolan i Jönköping, JTH, Datateknik och informatik.
    Löfström, Tuve
    Högskolan i Jönköping, JTH. Forskningsmiljö Datavetenskap och informatik.
    On the calibration of aggregated conformal predictors2017In: Proceedings of Machine Learning Research: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos, 2017, p. 154-173Conference paper (Refereed)
    Abstract [en]

    Conformal prediction is a learning framework that produces models that associate with each of their predictions a measure of statistically valid confidence. These models are typically constructed on top of traditional machine learning algorithms. An important result of conformal prediction theory is that the models produced are provably valid under relatively weak assumptions—in particular, their validity is independent of the specific underlying learning algorithm on which they are based. Since validity is automatic, much research on conformal predictors has been focused on improving their informational and computational efficiency. As part of the efforts in constructing efficient conformal predictors, aggregated conformal predictors were developed, drawing inspiration from the field of classification and regression ensembles. Unlike early definitions of conformal prediction procedures, the validity of aggregated conformal predictors is not fully understood—while it has been shown that they might attain empirical exact validity under certain circumstances, their theoretical validity is conditional on additional assumptions that require further clarification. In this paper, we show why validity is not automatic for aggregated conformal predictors, and provide a revised definition of aggregated conformal predictors that gains approximate validity conditional on properties of the underlying learning algorithm.

  • 30.
    Lundberg, Johannes
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Safe Kernel Programming with Rust2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Writing bug free computer code is a challenging task in a low-level language like C. While C compilers are getting better and better at detecting possible bugs, they still have a long way to go. For application programming we have higher level languages that abstract away details in memory handling and concurrent programming. However, a lot of an operating system's source code is still written in C and the kernel is exclusively written in C. How can we make writing kernel code safer? What are the performance penalties we have to pay for writing safe code? In this thesis, we will answer these questions using the Rust programming language. A Rust Kernel Programming Interface is designed and implemented, and a network device driver is then ported to Rust. The Rust code is analyzed to determine the safeness and the two implementations are benchmarked for performance and compared to each other. It is shown that a kernel device driver can be written entirely in safe Rust code, but the interface layer require some unsafe code. Measurements show unexpected minor improvements to performance with Rust.

  • 31. Magnusson, M.
    et al.
    Jonsson, L.
    Villani, M.
    Broman, David
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models2018In: Journal of Computational And Graphical Statistics, ISSN 1061-8600, E-ISSN 1537-2715, Vol. 27, no 2, p. 449-463Article in journal (Refereed)
    Abstract [en]

    Topic models, and more specifically the class of latent Dirichlet allocation (LDA), are widely used for probabilistic modeling of text. Markov chain Monte Carlo (MCMC) sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties. In particular, we propose and compare two different strategies for sampling the parameter block with latent topic indicators. The experiments show that the increase in statistical inefficiency from only partial collapsing is smaller than commonly assumed, and can be more than compensated by the speedup from parallelization and sparsity on larger corpora. We also prove that the partially collapsed samplers scale well with the size of the corpus. The proposed algorithm is fast, efficient, exact, and can be used in more modeling situations than the ordinary collapsed sampler. Supplementary materials for this article are available online.

  • 32.
    Mrazovic, Petar
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Crowdsensing-driven Route Optimisation Algorithms for Smart Urban Mobility2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Urban mobility is often considered as one of the main facilitators for greener and more sustainable urban development. However, nowadays it requires a significant shift towards cleaner and more efficient urban transport which would support for increased social and economic concentration of resources in cities. A high priority for cities around the world is to support residents’ mobility within the urban environments while at the same time reducing congestions, accidents, and pollution. However, developing a more efficient and greener (or in one word, smarter) urban mobility is one of the most difficult topics to face in large metropolitan areas. In this thesis, we approach this problem from the perspective of rapidly evolving ICT landscape which allow us to build mobility solutions without the need for large investments or sophisticated sensor technologies.

    In particular, we propose to leverage Mobile Crowdsensing (MCS) paradigm in which citizens use their mobile communication and/or sensing devices to collect, locally process and analyse, as well as voluntary distribute geo-referenced information. The mobility data crowdsensed from volunteer residents (e.g., events, traffic intensity, noise and air pollution, etc.) can provide valuable information about the current mobility conditions in the city, which can, with the adequate data processing algorithms, be used to route and manage people flows in urban environments.

    Therefore, in this thesis we combine two very promising Smart Mobility enablers – MCS and journey/route planning, and thus bring together to some extent distinct research challenges. We separate our research objectives into two parts, i.e., research stages: (1) architectural challenges in designing MCS systems and (2) algorithmic challenges in MCS-driven route planning applications. We aim to demonstrate a logical research progression over time, starting from fundamentals of human-in-the-loop sensing systems such as MCS, to route optimisation algorithms tailored for specific MCS applications. While we mainly focus on algorithms and heuristics to solve NP-hard routing problems, we use real-world application examples to showcase the advantages of the proposed algorithms and infrastructures.

  • 33.
    Natarajan, Saranya
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Broman, David
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. KTH Royal Inst Technol, Stockholm, Sweden..
    Timed C: An Extension to the C Programming Language for Real-Time Systems2018In: 24TH IEEE REAL-TIME AND EMBEDDED TECHNOLOGY AND APPLICATIONS SYMPOSIUM (RTAS 2018) / [ed] Pellizzoni, R, IEEE, 2018, p. 227-239Conference paper (Refereed)
    Abstract [en]

    The design and implementation of real-time systems require that both the logical and the temporal behavior are correct. There exist several specialized languages and tools that use the notion of logical time, as well as industrial strength languages such as Ada and RTJS that incorporate direct handling of real time. Although these languages and tools have shown to be good alternatives for safety-critical systems, most commodity real-time and embedded systems are today implemented in the standard C programming language. Such systems are typically targeting proprietary bare-metal platforms, standard POSIX compliant platforms, or open-source operating systems. It is, however, error prone to develop large, reliable, and portable systems based on these APIs. In this paper, we present an extension to the C programming language, called Timed C, with a minimal set of language primitives, and show how a retargetable source-to-source compiler can be used to compile and execute simple, expressive, and portable programs. To evaluate our approach, we conduct a case study of a CubeSat satellite. We implement the core timing aspects in Timed C, and show portability by compiling on-board software to both flight hardware, and to low-cost experimental platforms.

  • 34.
    Niazi, Salman
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Scaling Distributed Hierarchical File Systems Using NewSQL Databases2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    For many years, researchers have investigated the use of database technology to manage file system metadata, with the goal of providing extensible typed metadata and support for fast, rich metadata search. However, earlier attempts failed mainly due to the reduced performance introduced by adding database operations to the file system’s critical path. Recent improvements in the performance of distributed in-memory online transaction processing databases (NewSQL databases) led us to re-investigate the possibility of using a database to manage file system metadata, but this time for a distributed, hierarchical file system, the Hadoop Distributed File System (HDFS). The single-host metadata service of HDFS is a well-known bottleneck for both the size of the HDFS clusters and their throughput.In this thesis, we detail the algorithms, techniques, and optimizations used to develop HopsFS, an open-source, next-generation distribution of the HDFS that replaces the main scalability bottleneck in HDFS, single node in-memory metadata service, with a no-shared state distributed system built on a NewSQL database. In particular, we discuss how we exploit recent high-performance features from NewSQL databases, such as application-defined partitioning, partition pruned index scans, and distribution aware transactions, as well as more traditional techniques such as batching and write-ahead caches, to enable a revolution in distributed hierarchical file system performance.HDFS’ design is optimized for the storage of large files, that is, files ranging from megabytes to terabytes in size. However, in many production deployments of the HDFS, it has been observed that almost 20% of the files in the system are less than 4 KB in size and as much as 42% of all the file system operations are performed on files less than 16 KB in size. HopsFS introduces a tiered storage solution to store files of different sizes more efficiently. The tiers range from the highest tier where an in-memory NewSQL database stores very small files (<1 KB), to the next tier where small files (<64 KB) are stored in solid-state-drives (SSDs), also using a NewSQL database, to the largest tier, the existing Hadoop block storage layer for very large files. Our approach is based on extending HopsFS with an inode stuffing technique, where we embed the contents of small files with the metadata and use database transactions and database replication guarantees to ensure the availability, integrity, and consistency of the small files. HopsFS enables significantly larger cluster sizes, more than an order of magnitude higher throughput, and significantly lower client latencies for large clusters.Lastly, coordination is an integral part of the distributed file system operations protocols. We present a novel leader election protocol for partially synchronous systems that uses NewSQL databases as shared memory. Our work enables HopsFS, that uses a NewSQL database to save the operational overhead of managing an additional third-party service for leader election and deliver performance comparable to a leader election implementation using a state-of-the-art distributed coordination service, ZooKeeper.

  • 35.
    Niazi, Salman
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Ronström, Mikael
    Haridi, Seif
    KTH.
    Dowling, Jim
    KTH.
    Size Matters: Improving the Performance of Small Files in Hadoop2018Conference paper (Refereed)
    Abstract [en]

    The Hadoop Distributed File System (HDFS) is designed to handle massive amounts of data, preferably stored in very large files. The poor performance of HDFS in managing small files has long been a bane of the Hadoop community. In many production deployments of HDFS, almost 25% of the files are less than 16 KB in size and as much as 42% of all the file system operations are performed on these small files. We have designed an adaptive tiered storage using in-memory and on-disk tables stored in a high-performance distributed database to efficiently store and improve the performance of the small files in HDFS. Our solution is completely transparent, and it does not require any changes in the HDFS clients or the applications using the Hadoop platform. In experiments, we observed up to 61~times higher throughput in writing files, and for real-world workloads from Spotify our solution reduces the latency of reading and writing small files by a factor of 3.15 and 7.39 respectively.

  • 36.
    Olsson, Jakob
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Measuring the Technical and Process Benefits of Test Automation based on Machine Learning in an Embedded Device2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Learning-based testing is a testing paradigm that combines model-based testing with machine learning algorithms to automate the modeling of the SUT, test case generation, test case execution and verdict construction. A tool that implements LBT been developed at the CSC school at KTH called LBTest.

    LBTest utilizes machine learning algorithms with off-the-shelf equivalence- and model-checkers, and the modeling of user requirements by propositional linear temporal logic.

    In this study, it is be investigated whether LBT may be suitable for testing a micro bus architecture within an embedded telecommunication device. Furthermore ideas to further automate the testing process by designing a data model to automate user requirement generation are explored.

  • 37.
    Peiro Sajjad, Hooman
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Liu, Ying
    Stockholm University.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Optimizing Windowed Aggregation over Geo-Distributed Data Streams2018In: 2018 IEEE International Conference on Edge Computing (EDGE), IEEE Computer Society Digital Library, 2018, p. 33-41Conference paper (Refereed)
    Abstract [en]

    Real-time data analytics is essential since more and more applications require online decision making in a timely manner. However, efficient analysis of geo-distributed data streams is challenging. This is because data needs to be collected from all edge data centers, which aggregate data from local sources, in order to process most of the analytic tasks. Thus, most of the time edge data centers need to transfer data to a central data center over a wide area network, which is expensive. In this paper, we advocate for a coordinated approach of edge data centers in order to handle these analytic tasks efficiently and hence, reducing the communication cost among data centers. We focus on the windowed aggregation of data streams, which has been widely used in stream analytics. In general, aggregation of data streams among edge data centers in the same region reduces the amount of data that needs to be sent over cross-region communication links. Based on state-of-the-art research, we leverage intra-region links and design a low-overhead coordination algorithm that optimizes communication cost for data aggregation. Our algorithm has been evaluated using synthetic and Big Data Benchmark datasets. The evaluation results show that our algorithm reduces the bandwidth cost up to ~6x, as compared to the state-of-the-art solution.

  • 38.
    Rodriguez-Cancio, Marcelino
    et al.
    University of Rennes 1.
    Baudry, Benoit
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    White, Jules
    Vanderbildt University.
    Images of Code: Lossy Compression for Native Instructions2018Conference paper (Refereed)
    Abstract [en]

    Developers can use lossy compression on images and many other artifacts to reduce size and improve network transfer times. Native program instructions, however, are typically not considered candidates for lossy compression since arbitrary losses in instructions may dramatically affect program output. In this paper we show that lossy compression of compiled native instructions is possible in certain circumstances. We demonstrate that the instructions sequence of a program can be lossily translated into a separate but equivalent program with instruction-wise differences, which still produces the same output. We contribute the novel insight that it is possible to exploit such instruction differences to design lossy compression schemes for native code. We support this idea with sound and unsound program transformations that improve performance of compression techniques such as Run-Length (RLE), Huffman and LZ77. We also show that large areas of code can endure tampered instructions with no impact on the output, a result consistent with previous works from various communities.

  • 39.
    Soliman, Amira
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Graph-based Analytics for Decentralized Online Social Networks2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Decentralized Online Social Networks (DOSNs) have been introduced as a privacy preserving alternative to the existing online social networks.  DOSNs remove the dependency on a centralized provider and operate as distributed information management platforms. Current efforts of providing DOSNs are mainly focused on designing the required building blocks for managing the distributed network and supporting the social services (e.g., search, content delivery, etc.). However, there is a lack of reliable techniques for enabling complex analytical services (e.g., spam detection, identity validation, etc.) that comply with the decentralization requirements of DOSNs. In particular, there is a need for decentralized data analytic techniques and machine learning (ML) algorithms that can successfully run on top of DOSNs.

     

    In this thesis, we empower decentralized analytics for DOSNs through a set of novel algorithms. Our algorithms allow decentralized analytics to effectively work on top of fully decentralized topology, when the data is fully distributed and nodes have access to their local knowledge only. Furthermore, our algorithms and methods are able to extract and exploit the latent patterns in the social user interaction networks and effectively combine them with the shared content, yielding significant improvements for the complex analytic tasks. We argue that, community identification is at the core of the learning and analytical services provided for DOSNs. We show in this thesis that knowledge on community structures and information dissemination patterns, embedded in the topology of social networks has a potential to greatly enhance data analytic insights and improve results. At the heart of this thesis lies a community detection technique that successfully extracts communities in a completely decentralized manner. In particular, we show that multiple complex analytic tasks, like spam detection and identity validation,  can be successfully tackled by harvesting the information from the social network structure. This is achieved by using decentralized community detection algorithm which acts as the main building block for the community-aware learning paradigm that we lay out in this thesis. To the best of our knowledge, this thesis represents the first attempt to bring complex analytical services, which require decentralized iterative computation over distributed data, to the domain of DOSNs. The experimental evaluation of our proposed algorithms using real-world datasets confirms the ability of our solutions to generate  efficient ML models in massively parallel and highly scalable manner.

  • 40.
    Soliman, Amira
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Stad: Stateful Diffusion for Linear Time Community DetectionManuscript (preprint) (Other academic)
    Abstract [en]

    Community detection is one of the preeminent topics in network analysis. Communities in real-world networks vary in their characteristics, such as their internal cohesion and size. Despite a large variety of methods proposed to detect communities so far, most of existing approaches fall into the category of global approaches. Specifically, these global approaches adapt their detection model focusing on approximating the global structure of the whole network, instead of performing approximation at the communities level. Global techniques tune their parameters to "one size fits all" model, so they are quite successful with extracting communities in homogeneous cases but suffer in heterogeneous community size distributions.

    In this paper, we present a stateful diffusion approach (Stad) for community detection that employs diffusion. Stad boosts diffusion with conductance-based function that acts like a tuning parameter to control the diffusion speed. In contrast to existing diffusion mechanisms which operate with global and fixed speed, Stad introduces stateful diffusion to treat every community individually. Particularly, Stad controls the diffusion speed at node level, such that each node determines the diffusion speed associated with every possible community membership independently. Thus, Stad is able to extract communities more accurately in heterogeneous cases by dropping "one size fits all" model. Furthermore, Stad employs a vertex-centric approach which is fully decentralized and highly scalable, and requires no global knowledge. So as, Stad can be successfully applied in distributed environments, such as large-scale graph processing or decentralized machine learning. The results with both real-world and synthetic datasets show that Stad outperforms the state-of-the-art techniques, not only in the community size scale issue but also by achieving higher accuracy that is twice the accuracy achieved by the state-of-the-art techniques.

  • 41.
    Soliman, Amira
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Rahimian, Fatemeh
    RISE SICS.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Stad: Stateful Diffusion for Linear Time Community Detection2018In: 38th IEEE International Conference on Distributed Computing Systems, 2018Conference paper (Refereed)
    Abstract [en]

    Community detection is one of the preeminent topics in network analysis. Communities in real-world networks vary in their characteristics, such as their internal cohesion and size. Despite a large variety of methods proposed to detect communities so far, most of existing approaches fall into the category of global approaches. Specifically, these global approaches adapt their detection model focusing on approximating the global structure of the whole network, instead of performing approximation at the communities level. Global techniques tune their parameters to “one size fits all” model, so they are quite successful with extracting communities in homogeneous cases but suffer in heterogeneous community size distributions. In this paper, we present a stateful diffusion approach (Stad) for community detection that employs diffusion. Stad boosts diffusion with a conductance-based function that acts like a tuning parameter to control the diffusion speed. In contrast to existing diffusion mechanisms which operate with global and fixed speed, Stad introduces stateful diffusion to treat every community individually. Particularly, Stad controls the diffusion speed at node level, such that each node determines the diffusion speed associated with every possible community membership independently. Thus, Stad is able to extract communities more accurately in heterogeneous cases by dropping “one size fits all” model. Furthermore, Stad employs a vertex-centric approach which is fully decentralized and highly scalable, and requires no global knowledge. So as, Stad can be successfully applied in distributed environments, such as large-scale graph processing or decentralized machine learning. The results with both real-world and synthetic datasets show that Stad outperforms the state-of-the-art techniques, not only in the community size scale issue but also by achieving higher accuracy that is twice the accuracy achieved by the state-of-the-art techniques.

  • 42.
    Soto-Valero, César
    et al.
    Universidad Central de Las Villas.
    Bourcier, Johann
    University of Rennes.
    Baudry, Benoit
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Detection and Analysis of Behavioral T-patternsi n Debugging Activities2018Conference paper (Refereed)
  • 43. Thomas, Denez
    et al.
    Harrand, Nicolas
    Baudry, Benoit
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Bossis, Bruno
    Code{strata} sonifying software complexity2018In: TEI 2018 - Proceedings of the 12th International Conference on Tangible, Embedded, and Embodied Interaction, Association for Computing Machinery (ACM), 2018, p. 617-621Conference paper (Refereed)
    Abstract [en]

    Code{strata} is an interdisciplinary collaboration between art studies researchers (Rennes 2) and computer scientists (INRIA, KTH). It is a sound installation: a computer system unit made of concrete that sits on a wooden desk. The purpose of this project is to question the opacity and simplicity of high-level interfaces used in daily gestures. It takes the form of a 3-D sonification of a full software trace that is collected when performing a copy and paste command in a simple text editor. The user may hear, through headphones, a poetic interpretation of what happens in a computer, behind the of graphical interfaces. The sentence "Copy and paste" is played back in as many pieces as there are nested functions called during the execution of the command.

  • 44.
    Yalew, Sileshi Demesie
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Mobile Device Security with ARM TrustZone2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Mobile devices such as smartphones are becoming the majority of computing devices due to their evolving capabilities. Currently, service providers such as nancial and healthcare institutions oer services to their clients using smartphone applications (apps). Many of these apps run on Android, the most adopted mobile operating system (OS) today. Since smartphones are designed to be carried around all the time, many persons use them to store their private data. However, the popularity of Android and the open nature of its app marketplaces make it a prime target for malware. This situation puts data stored in smartphones in jeopardy, as it can be stealthily stolen or modied by malware that infects the device.

    With the increasing popularity of smartphones and the increasing amount of personal data  stored on these devices, mobile device security has drawn signicant attention from both industry and academia. As a result, several security mechanisms and tools such as anti-malware software have been proposed for mobile OSs to improve the privacy of private data and to mitigate some of the security risks associated with mobile devices. However, these tools and mechanisms run in the device and assume that the mobile OS is trusted, i.e., that it is part of the trusted computing base (TCB). However, current malware often disables anti-malware software when it infects a device. For mobile phones this trend started more than a decade ago with malware such as the Metal Gear Trojan and Cabir.M, and continues to this day, e.g., with HijackRAT. In this work, we use the ARM TrustZone, a security extension for ARM processors that provides a hardware-assisted isolated environment, to implement security services that are protected from malware even if the mobile OS is compromised.

    In this thesis, we investigate two approaches to address some of the security risks associated with Android-based devices. In the rst approach, we present security services to detect intrusions in mobile devices. We design and implement services for posture assessment (which evaluates the level of trust we can have in the device), for dynamic analysis (which performs dynamic (runtime) analysis of apps using traces of Android application programming interface (API) function calls and kernel syscalls to detect apps for malware), and for authenticity detection (which provides assurance of the authenticity and integrity of apps running on mobile devices). In the second approach, we design and implement a backup and recovery system to protect mobile devices from attacks caused by ransomware attacks, system errors, etc. Finally, we develop a software framework to facilitate the development of security services for mobile devices by combining components of the above services. As proof-of-concept, we implemented a prototype for each service and made experimental evaluations using an i.MX53 development board with an ARM processor with TrustZone.

  • 45.
    Yalew, Sileshi Demesie
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. Univ Lisbon, Inst Super Tecn, INESD ID, Lisbon, Portugal.
    Mendonca, Pedro
    Maguire Jr., Gerald Q.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Haridi, Seif
    KTH, School of Information and Communication Technology (ICT).
    Correia, Miguel
    TruApp: A TrustZone-based Authenticity Detection Service for Mobile Apps2017In: 2017 IEEE 13TH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB), IEEE , 2017, p. 791-799Conference paper (Refereed)
    Abstract [en]

    In less than a decade, mobile apps became an integral part of our lives. In several situations it is important to provide assurance that a mobile app is authentic, i.e., that it is indeed the app produced by a certain company. However, this is challenging, as such apps can be repackaged, the user malicious, or the app tampered with by an attacker. This paper presents the design of TRUAPP, a software authentication service that provides assurance of the authenticity and integrity of apps running on mobile devices. TRUAPP provides such assurance, even if the operating system is compromised, by leveraging the ARM TrustZone hardware security extension. TRUAPP uses a set of techniques (static watermarking, dynamic watermarking, and cryptographic hashes) to verify the integrity of the apps. The service was implemented in a hardware board that emulates a mobile device, which was used to do a thorough experimental evaluation of the service.

  • 46.
    Zanni-Merk, Cecilia
    et al.
    Natl Inst Appl Sci Rouen Normandie, LITIS Lab, Normandy, France.;Natl Inst Appl Sci Rouen Normandie, MIND Team, Normandy, France..
    Frydman, Claudia
    Aix Marseille Univ, Marseille, France..
    Håkansson, Anne
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. KTH Royal Inst Technol, Stockholm, Sweden..
    Special Issue: Advances in Knowledge-Based and Intelligent Engineering and Information Systems Preface2018In: DATA TECHNOLOGIES AND APPLICATIONS, ISSN 2514-9288, Vol. 52, no 4, p. 462-462Article in journal (Other academic)
  • 47.
    Zeng, Jingna
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. INESC-ID/Instituto Superior Tecnico, Universidade de Lisboa, Portugal.
    Romano, P.
    Barreto, J.
    Rodrigues, L.
    Haridi, S.
    Online tuning of parallelism degree in parallel nesting transactional memory2018In: Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 474-483, article id 8425201Conference paper (Refereed)
    Abstract [en]

    This paper addresses the problem of self-Tuning the parallelism degree in Transactional Memory (TM) systems that support parallel nesting (PN-TM). This problem has been long investigated for TMs not supporting nesting, but, to the best of our knowledge, has never been studied in the context of PN-TMs. Indeed, the problem complexity is inherently exacerbated in PN-TMs, since these require to identify the optimal parallelism degree not only for top-level transactions but also for nested sub-Transactions. The increase of the problem dimensionality raises new challenges (e.g., increase of the search space, and proneness to suffer from local maxima), which are unsatisfactorily addressed by self-Tuning solutions conceived for flat nesting TMs. We tackle these challenges by proposing AUTOPN, an on-line self-Tuning system that combines model-driven learning techniques with localized search heuristics in order to pursue a twofold goal: i) enhance convergence speed by identifying the most promising region of the search space via model-driven techniques, while ii) increasing robustness against modeling errors, via a final local search phase aimed at refining the model's prediction. We further address the problem of tuning the duration of the monitoring windows used to collect feedback on the system's performance, by introducing novel, domain-specific, mechanisms aimed to strike an optimal trade-off between latency and accuracy of the self-Tuning process. We integrated AUTOPN with a state of the art PN-TM (JVSTM) and evaluated it via an extensive experimental study. The results of this study highlight that AUTOPN can achieve gains of up to 45× in terms of increased accuracy and 4× faster convergence speed, when compared with several on-line optimization techniques (gradient descent, simulated annealing and genetic algorithm), some of which were already successfully used in the context of flat nesting TMs.

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