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  • 1.
    Aerts, Arend
    et al.
    Eindhoven University of Technology, Eindhoven, The Netherlands.
    Reniers, Michel A.
    Eindhoven University of Technology, Eindhoven, The Netherlands.
    Mousavi, Mohammad Reza
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Model-Based Testing of Cyber-Physical Systems2016Inngår i: Cyber-Physical Systems: Foundations, Principles and Applications / [ed] H. Song, D.B. Rawat, S. Jeschke, and Ch. Brecher, Saint Louis: Elsevier, 2016, s. 287-304Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Cyber-physical systems (CPSs) are the result of the integration of connected computer systems with the physical world. They feature complex interactions that go beyond traditional communication schemes and protocols in computer systems. One distinguished feature of such complex interactions is the tight coupling between discrete and continuous interactions, captured by hybrid system models.

    Due to the complexity of CPSs, providing rigorous and model-based analysis methods and tools for verifying correctness of such systems is of the utmost importance. Model-based testing (MBT) is one such verification technique that can be used for checking the conformance of an implementation of a system to its specification (model).

    In this chapter, we first review the main concepts and techniques in MBT. Subsequently, we review the most common modeling formalisms for CPSs, with focus on hybrid system models. Subsequently, we provide a brief overview of conformance relations and conformance testing techniques for CPSs. © 2017 Elsevier Inc. All rights reserved.

  • 2.
    Afrim, Cerimi
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Norén, Joakim
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Motåtgärder vid IT-forensisk liveanalys2011Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [sv]

    Liveanalys är ett begrepp som i detta arbete innebär att man undersöker ett datorsystem under tiden det är igång. Detta kan göras av flera skäl, t.ex. när det är risk för att kryptering finns på systemet vilket kan aktiveras när det stängs ner. Annars är det vanligt om man vill undersöka nätverkskopplingar, aktiva processer eller andra företeelser som kan vara volatila, dvs. försvinner när systemet stängs ner. Detta arbete kommer att ha fokus på motåtgärder vid forensisk liveanalys och redogöra för olika metoder och strategier som kan användas för dessa motåtgärder. Vi har bland annat skrivit ett program som automatiskt stänger ner systemet när man sätter i ett USB-minne eller annan media. Dessa media är oftast de man har sina forensiska program på när man ska göra en liveanalys. Andra viktiga element i arbetet är användning av kryptering, tidstämplar och sabotagekod för att försvåra liveanalysen. Vår analys i ämnet visar att det är relativt enkelt att förhindra att en liveanalys kan utföras på ett tillförlitligt sätt.

    Fulltekst (pdf)
    fulltext
  • 3.
    Agelis, Sacki
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Jonsson, Magnus
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Optoelectronic router with a reconfigurable shuffle network based on micro-optoelectromechanical systems2004Inngår i: Journal of Optical Networking, ISSN 1536-5379, Vol. 4, nr 1, s. 1-10Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    An optoelectronic router with a shuffle exchange network is presented and enhanced by the addition of micro-optoelectromechanical systems (MOEMS) in the network to add the ability to reconfigure the shuffle network. The MOEMS described here are fully connected any-to-any crossbar switches. The added reconfigurability provides the opportunity to adapt the system to different common application characteristics. Two representative application models are described: The first has symmetric properties, and the second has asymmetric properties. The router system is simulated with the specified applications and an analysis of the results is carried out. By use of MOEMS in the optical network, and thus reconfigurability, greater than 50% increased throughput performance and decreased average packet delay are obtained for the given application. Network congestion is avoided throughout the system if reconfigurability is used.

    Fulltekst (pdf)
    fulltext
  • 4.
    Ahmed, Iftikhar
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Farooq, Muhammad
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Switched Multi-hop Priority Queued Networks-Influence of priority levels on Soft Real-time Performance2010Independent thesis Advanced level (degree of Master (One Year)), 15 poäng / 22,5 hpOppgave
    Abstract [en]

    In the last few years, the number of real-time applications has increased. These applications are sensitive and require the methods to utilize existing network capacity efficiently to meet performance requirements and achieve the maximum throughput to overcome delay, jitter and packet loss. In such cases, when the network needs to support highly interactive traffic like packet-switched voice, the network congestion is an issue that can lead to various problems. If the level of congestion is high enough, the users may not be able to complete their calls and have existing calls dropped or may experience a variety of delays that make it difficult to participate smooth conversation.

    In this paper, we investigate the effect of priority levels on soft real-time performance. We use the priority queues to help us manage the congestion, handle the interactive traffic and improve the over all performance of the system. We consider switched multi-hop network with priority queues. All the switches and end-nodes control the real-time traffic with “Earlier Deadline First” scheduling. The performance of the network is characterized in terms of the average delay, the deadline missing ratio and the throughput.

    We will analyze these parameters with both the bursty traffic and evenly distributed traffic. We will analyze different priority levels and will see how the increase in priority level increases the performance of the soft real-time system.

    Fulltekst (pdf)
    FULLTEXT01
  • 5.
    Aichernig, Bernhard K.
    et al.
    Graz University of Technology, Graz, Austria.
    Mostowski, Wojciech
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Mousavi, Mohammad Reza
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES). Department of Informatics, University of Leicester, Leicester, UK.
    Tappler, Martin
    Graz University of Technology, Graz, Austria.
    Taromirad, Masoumeh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Model Learning and Model-Based Testing2018Inngår i: Machine Learning for Dynamic Software Analysis: Potentials and Limits / [ed] Amel Bennaceur, Reiner Hähnle, Karl Meinke, Heidelberg: Springer, 2018, s. 74-100Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We present a survey of the recent research efforts in integrating model learning with model-based testing. We distinguished two strands of work in this domain, namely test-based learning (also called test-based modeling) and learning-based testing. We classify the results in terms of their underlying models, their test purpose and techniques, and their target domains. © Springer International Publishing AG

  • 6.
    Al Khatib, Sultan M.
    et al.
    Al-balqa Applied University, Al Salt, Jordan.
    Alkharabsheh, Khalid
    Al-balqa Applied University, Al Salt, Jordan.
    Alawadi, Sadi
    Högskolan i Halmstad, Akademin för informationsteknologi. Uppsala University, Uppsala, Sweden.
    Selection of human evaluators for design smell detection using dragonfly optimization algorithm: An empirical study2023Inngår i: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 155, artikkel-id 107120Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Context: Design smell detection is considered an efficient activity that decreases maintainability expenses and improves software quality. Human context plays an essential role in this domain. Objective: In this paper, we propose a search-based approach to optimize the selection of human evaluators for design smell detection. Method: For this purpose, Dragonfly Algorithm (DA) is employed to identify the optimal or near-optimal human evaluator's profiles. An online survey is designed and asks the evaluators to evaluate a sample of classes for the presence of god class design smell. The Kappa-Fleiss test has been used to validate the proposed approach. Results: The results show that the dragonfly optimization algorithm can be utilized effectively to decrease the efforts (time, cost ) of design smell detection concerning the identification of the number and the optimal or near-optimal profile of human experts required for the evaluation process. Conclusions: A Search-based approach can be effectively used for improving a god-class design smell detection. Consequently, this leads to minimizing the maintenance cost. © 2022 The Author(s)

  • 7.
    Alabdallah, Abdallah
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Jakubowski, Jakub
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Bobek, Szymon
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Nalepa, Grzegorz J.
    Understanding Survival Models through Counterfactual ExplanationsManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    The development of black-box survival models has created a need for methods that explain their outputs, just as in the case of traditional machine learning methods. Survival models usually predict functions rather than point estimates. This special nature of their output makes it more difficult to explain their operation. We propose a method to generate plausible counterfactual explanations for survival models. The method supports two options that handle the special nature of survival models' output. One option relies on the Survival Scores, which are based on the area under the survival function, which is more suitable for proportional hazard models. The other one relies on Survival Patterns in the predictions of the survival model, which represent groups that are significantly different from the survival perspective. This guarantees an intuitive well-defined change from one risk group (Survival Pattern) to another and can handle more realistic cases where the proportional hazard assumption does not hold. The method uses a Particle Swarm Optimization algorithm to optimize a loss function to achieve four objectives: the desired change in the target, proximity to the explained example, likelihood, and the actionability of the counterfactual example. Two predictive maintenance datasets and one medical dataset are used to illustrate the results in different settings. The results show that our method produces plausible counterfactuals, which increase the understanding of black-box survival models.

  • 8.
    Alabdallah, Abdallah
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Lund University, Lund, Sweden.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi. RISE Research Institutes of Sweden, Stockholm, Sweden.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi.
    The Concordance Index Decomposition: A Measure for a Deeper Understanding of Survival Prediction Models2024Inngår i: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 148, s. 1-10, artikkel-id 102781Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. This paper proposes a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a more fine-grained analysis of the strengths and weaknesses of survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against state-of-the-art and classical models, together with a new variational generative neural-network-based method (SurVED), which is also proposed in this paper. Performance is assessed using four publicly available datasets with varying levels of censoring. The analysis using the C-index decomposition and synthetic censoring shows that deep learning models utilize the observed events more effectively than other models, allowing them to keep a stable C-index in different censoring levels. In contrast, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events. © 2024 The Author(s)

  • 9.
    Alabdallah, Abdallah
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR). RISE Research Institutes of Sweden.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR). Lund University, Lund, Sweden.
    SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP2022Inngår i: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) / [ed] Joshua Zhexue Huang; Yi Pan; Barbara Hammer; Muhammad Khurram Khan; Xing Xie; Laizhong Cui; Yulin He, Piscataway, NJ: IEEE, 2022Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Survival Analysis models usually output functions (survival or hazard functions) rather than point predictions like regression and classification models. This makes the explanations of such models a challenging task, especially using the Shapley values. We propose SurvSHAP, a new model-agnostic algorithm to explain survival models that predict survival curves. The algorithm is based on discovering patterns in the predicted survival curves, the output of the survival model, that would identify significantly different survival behaviors, and utilizing a proxy model and SHAP method to explain these distinct survival behaviors. Experiments on synthetic and real datasets demonstrate that the SurvSHAP is able to capture the underlying factors of the survival patterns. Moreover, SurvSHAP results on the Cox Proportional Hazard model are compared with the weights of the model to show that we provide faithful overall explanations, with more fine-grained explanations of the sub-populations. We also illustrate the wrong model and explanations learned by a Cox model when applied to heterogeneous sub-populations. We show that a non-linear machine learning survival model with SurvSHAP can better model the data and provide better explanations than linear models.

  • 10.
    Alabdallah, Abdallah
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Fan, Yuantao
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Discovering Premature Replacements in Predictive Maintenance Time-to-Event Data2023Inngår i: Proceedings of the Asia Pacific Conference of the PHM Society 2023 / [ed] Takehisa Yairi; Samir Khan; Seiji Tsutsumi, New York: The Prognostics and Health Management Society , 2023, Vol. 4Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Time-To-Event (TTE) modeling using survival analysis in industrial settings faces the challenge of premature replacements of machine components, which leads to bias and errors in survival prediction. Typically, TTE survival data contains information about components and if they had failed or not up to a certain time. For failed components, the time is noted, and a failure is referred to as an event. A component that has not failed is denoted as censored. In industrial settings, in contrast to medical settings, there can be considerable uncertainty in an event; a component can be replaced before it fails to prevent operation stops or because maintenance staff believe that the component is faulty. This shows up as “no fault found” in warranty studies, where a significant proportion of replaced components may appear fault-free when tested or inspected after replacement.

    In this work, we propose an expectation-maximization-like method for discovering such premature replacements in survival data. The method is a two-phase iterative algorithm employing a genetic algorithm in the maximization phase to learn better event assignments on a validation set. The learned labels through iterations are accumulated and averaged to be used to initialize the following expectation phase. The assumption is that the more often the event is selected, the more likely it is to be an actual failure and not a “no fault found”.

    Experiments on synthesized and simulated data show that the proposed method can correctly detect a significant percentage of premature replacement cases.

    Fulltekst (pdf)
    fulltext
  • 11.
    Alendal, Gunnar
    et al.
    NTNU, Gjøvik, Norway.
    Dyrkolbotn, Geir Olav
    NTNU, Gjøvik, Norway & Norwegian Defence Cyber Academy (NDCA), Jørstadmoen, Norway.
    Axelsson, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Forensics acquisition – Analysis and circumvention of samsung secure boot enforced common criteria mode2018Inngår i: Digital Investigation. The International Journal of Digital Forensics and Incident Response, ISSN 1742-2876, E-ISSN 1873-202X, Vol. 24, nr Suppl., s. S60-S67Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The acquisition of data from mobile phones have been a mainstay of criminal digital forensics for a number of years now. However, this forensic acquisition is getting more and more difficult with the increasing security level and complexity of mobile phones (and other embedded devices). In addition, it is often difficult or impossible to get access to design specifications, documentation and source code. As a result, the forensic acquisition methods are also increasing in complexity, requiring an ever deeper understanding of the underlying technology and its security mechanisms. Forensic acquisition techniques are turning to more offensive solutions to bypass security mechanisms, through security vulnerabilities. Common Criteria mode is a security feature that increases the security level of Samsung devices, and thus make forensic acquisition more difficult for law enforcement. With no access to design documents or source code, we have reverse engineered how the Common Criteria mode is actually implemented and protected by Samsung's secure bootloader. We present how this security mode is enforced, security vulnerabilities therein, and how the discovered security vulnerabilities can be used to circumvent Common Criteria mode for further forensic acquisition. © 2018 The Author(s). Published by Elsevier Ltd on behalf of DFRWS.

  • 12.
    Alfredsson, Erika
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Bengtsson, Matilda
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Bluetooth-implementation för Netbiter EC3502014Independent thesis Basic level (university diploma), 10 poäng / 15 hpOppgave
    Abstract [en]

    In industries today the demand for reading the state of industrial equipment and thus prevent machine breakdown, is increasing. The company HMS Industrial Networks AB has a product on the market, Netbiter EC350 that is used to read sensors and thus find out the condition of industrial equipment. By reporting scanned data to users through a cloud service, users can keep track of their equipment.

     

    When developing Netbiter EC350 a slot was made for a Bluetooth module to offer clients a wireless reading in future developments. In this project a prototype was made to show how this Bluetooth communication can be implemented.

     

    The goal of the project was to create a Bluetooth communication between a Bluetooth sensor and Netbiter EC350. A user interface was made to allow the user to read sensor values.

     

    The result of the project shows how a Bluetooth communication can be implemented to read sensors wireless and therefore it fulfills its purpose and goal. The user can find connectable Bluetooth devices, connect to a device and read measured values through a user interface.

     

    The prototype demonstrates how a Bluetooth communication with a Netbiter EC350 can be implemented and the project is therefore considered to be a good basis for future development.

    Fulltekst (pdf)
    fulltext
  • 13.
    Ali Fareedi, Abid
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Ismail, Muhammad
    University of Glamorgan, Pontypridd, United Kingdom.
    Ghazawneh, Ahmad
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Bergquist, Magnus
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Ortiz-Rodriguez, Fernando
    Universidad Autónoma De Tamaulipas, Ciudad Victoria, Mexico.
    The Utilization of Artificial Intelligence for Developing Autonomous Social Robots within Health Information Systems2023Inngår i: CEUR Workshop Proceedings / [ed] Tiwari, Sanju et al., CEUR-WS , 2023, Vol. 3447, s. 34-50Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This study focuses on using AI systems, specifically conversational agents (CAs), to improve information flow during peak hours in healthcare emergency departments (EDs). We customized a Cross Industry Standard Process for Data Mining CRISP-DM approach to a CRISP-Knowledge graph (CRISP-KG) for overall design research. We use a knowledge graph approach to create an intelligent knowledge base (KBs) for CAs, which can enhance their reasoning, knowledge management, and context awareness abilities. We employ a collaborative methodology and ontology design patterns to develop a formal ontological model. Our goal is to build intelligent KBs for CAs that can interact with end-users and improve care quality in EDs, using Semantic Web Rule Language (SWRL) for inference. The KG approach can assist healthcare practitioners and patients in managing information flow more efficiently in EDs, ultimately improving care outcomes. © 2023 CEUR-WS. All rights reserved.

  • 14.
    Ali Hamad, Rebeen
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Järpe, Eric
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Lundström, Jens
    JeCom Consulting, Halmstad, Sweden.
    Stability analysis of the t-SNE algorithm for human activity pattern data2018Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Health technological systems learning from and reacting on how humans behave in sensor equipped environments are today being commercialized. These systems rely on the assumptions that training data and testing data share the same feature space, and residing from the same underlying distribution - which is commonly unrealistic in real-world applications. Instead, the use of transfer learning could be considered. In order to transfer knowledge between a source and a target domain these should be mapped to a common latent feature space. In this work, the dimensionality reduction algorithm t-SNE is used to map data to a similar feature space and is further investigated through a proposed novel analysis of output stability. The proposed analysis, Normalized Linear Procrustes Analysis (NLPA) extends the existing Procrustes and Local Procrustes algorithms for aligning manifolds. The methods are tested on data reflecting human behaviour patterns from data collected in a smart home environment. Results show high partial output stability for the t-SNE algorithm for the tested input data for which NLPA is able to detect clusters which are individually aligned and compared. The results highlight the importance of understanding output stability before incorporating dimensionality reduction algorithms into further computation, e.g. for transfer learning.

    Fulltekst (pdf)
    tsne-stability
  • 15.
    Ali Hamad, Rebeen
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Kimura, Masashi
    Convergence Lab, Tokyo, Japan.
    Lundström, Jens
    Convergia Consulting, Halmstad, Sweden.
    Efficacy of Imbalanced Data Handling Methods on Deep Learning for Smart Homes Environments2020Inngår i: SN Computer Science, ISSN 2661-8907, Vol. 1, nr 4, artikkel-id 204Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Human activity recognition as an engineering tool as well as an active research field has become fundamental to many applications in various fields such as health care, smart home monitoring and surveillance. However, delivering sufficiently robust activity recognition systems from sensor data recorded in a smart home setting is a challenging task. Moreover, human activity datasets are typically highly imbalanced because generally certain activities occur more frequently than others. Consequently, it is challenging to train classifiers from imbalanced human activity datasets. Deep learning algorithms perform well on balanced datasets, yet their performance cannot be promised on imbalanced datasets. Therefore, we aim to address the problem of class imbalance in deep learning for smart home data. We assess it with Activities of Daily Living recognition using binary sensors dataset. This paper proposes a data level perspective combined with a temporal window technique to handle imbalanced human activities from smart homes in order to make the learning algorithms more sensitive to the minority class. The experimental results indicate that handling imbalanced human activities from the data-level outperforms algorithms level and improved the classification performance. © The Author(s) 2020

    Fulltekst (pdf)
    fulltext
  • 16.
    Ali, Hazem
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Patoary, Mohammad Nazrul Ishlam
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Design and Implementation of an Audio Codec (AMR-WB) using Dataflow Programming Language CAL in the OpenDF Environment2010Independent thesis Advanced level (degree of Master (Two Years)), 30 poäng / 45 hpOppgave
    Abstract [en]

    Over the last three decades, computer architects have been able to achieve an increase in performance for single processors by, e.g., increasing clock speed, introducing cache memories and using instruction level parallelism. However, because of power consumption and heat dissipation constraints, this trend is going to cease. In recent times, hardware engineers have instead moved to new chip architectures with multiple processor cores on a single chip. With multi-core processors, applications can complete more total work than with one core alone. To take advantage of multi-core processors, we have to develop parallel applications that assign tasks to different cores. On each core, pipeline, data and task parallelization can be used to achieve higher performance. Dataflow programming languages are attractive for achieving parallelism because of their high-level, machine-independent, implicitly parallel notation and because of their fine-grain parallelism. These features are essential for obtaining effective, scalable utilization of multi-core processors.

    In this thesis work we have parallelized an existing audio codec - Adaptive Multi-Rate Wide Band (AMR-WB) - written in the C language for single core processor. The target platform is a multi-core AMR11 MP developer board. The final result of the efforts is a working AMR-WB encoder implemented in CAL and running in the OpenDF simulator. The C specification of the AMR-WB encoder was analysed with respect to dataflow and parallelism. The final implementation was developed in the CAL Actor Language, with the goal of exposing available parallelism - different dataflows - as well as removing unwanted data dependencies. Our thesis work discusses mapping techniques and guidelines that we followed and which can be used in any future work regarding mapping C based applications to CAL. We also propose solutions for some specific dependencies that were revealed in the AMR-WB encoder analysis and suggest further investigation of possible modifications to the encoder to enable more efficient implementation on a multi-core target system.

    Fulltekst (pdf)
    FULLTEXT01
  • 17.
    Aljarbouh, Ayman
    et al.
    Centre de Recherche INRIA, Rennes, France.
    Zeng, Yingfu
    Rice University, Houston, Texas, United States.
    Duracz, Adam
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Caillaud, Benoît
    Centre de Recherche INRIA, Rennes, France.
    Taha, Walid
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES). Rice University, Houston, Texas, United States.
    Chattering-Free Simulation for Hybrid Dynamical Systems: Semantics and Prototype Implementation2016Inngår i: 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES) / [ed] Randall Bilof, Los Alamitos: IEEE Computer Society, 2016, s. 412-422, artikkel-id 7982279Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Chattering is a fundamental phenomenon that is unique to hybrid systems, due to the complex interaction between discrete dynamics (in the form of discrete transitions) and continuous dynamics (in the form of time). In practice, simulating chattering hybrid systems is challenging in that simulation effectively halts near the chattering time point, as an infinite number of discrete transitions would need to be simulated. In this paper, formal conditions are provided for when the simulated models of hybrid systems display chattering behavior, and methods are proposed for avoiding chattering “on the fly” in runtime. We utilize dynamical behavior analysis to derive conditions for detecting chattering without enumeration of modes. We also present a new iterative algorithm to allow for solutions to be carried past the chattering point, and we show by a prototypical implementation how to generate the equivalent chattering-free dynamics internally by the simulator in the main simulation loop. The concepts are illustrated with examples throughout the paper. © 2016 IEEE.

  • 18.
    Aljoundi, Ahmad
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Abukarsh, Wael
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Testrigg för att hantera NFC-taggar och QR-koder2022Independent thesis Basic level (professional degree), 10 poäng / 15 hpOppgave
    Abstract [sv]

    Automatisering av testprocesser är viktigt eftersom manuella tester är komplicerade och tidskrävande. Testarbetet effektiviseras och kvaliteten kan höjas genom att automatisera testprocesserna. Arbetet som beskrivs i det här examensarbetet har utförts på Phoniro AB i Halmstad och syftet med projektet är att identifiera och konstruera en helautomatiserad lösning för skanningen av NFC-taggar och QR-koder. I arbetet konstrueras en mekanisk testrigg som är en lämplig lösning baserat på framtagna krav. För att välja den mest lämpliga mjukvaru- och hårdvaruplattformen för testriggen utifrån kraven användes utvärderingsmatriser. Testriggen består av tre delar, en 3D-modell av testriggen, ett kretskort som är testriggens kontroller och mjukvarudel för att programmera testriggen och integrera den med företagets testramverk. Därefter har testriggen byggts upp, alla delar kopplats ihop och programmerats för att testas mot de krav som ställts upp. Testriggen som har tagits fram är en prototyp som har en stor utvecklingspotential med tanke på framtida utveckling

    Fulltekst (pdf)
    fulltext
  • 19.
    Alkhabbas, Fahed
    et al.
    Malmö University, Malmö, Sweden.
    Alawadi, Sadi
    Högskolan i Halmstad, Akademin för informationsteknologi. Blekinge Institute of Technology, Karlskrona, Sweden.
    Ayyad, Majed
    Birzeit University, Birzeit, Palestine.
    Spalazzese, Romina
    Malmö University, Malmö, Sweden.
    Davidsson, Paul
    Malmö University, Malmö, Sweden.
    ART4FL: An Agent-based Architectural Approach for Trustworthy Federated Learning in the IoT2023Inngår i: 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC), IEEE, 2023, s. 270-275Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The integration of the Internet of Things (IoT) and Machine Learning (ML) technologies has opened up for the development of novel types of systems and services. Federated Learning (FL) has enabled the systems to collaboratively train their ML models while preserving the privacy of the data collected by their IoT devices and objects. Several FL frameworks have been developed, however, they do not enable FL in open, distributed, and heterogeneous IoT environments. Specifically, they do not support systems that collect similar data to dynamically discover each other, communicate, and negotiate about the training terms (e.g., accuracy, communication latency, and cost). Towards bridging this gap, we propose ART4FL, an end-to-end framework that enables FL in open IoT settings. The framework enables systems’ users to configure agents that participate in FL on their behalf. Those agents negotiate and make commitments (i.e., contractual agreements) to dynamically form federations. To perform FL, the framework deploys the needed services dynamically, monitors the training rounds, and calculates agents’ trust scores based on the established commitments. ART4FL exploits a blockchain network to maintain the trust scores, and it provides those scores to negotiating agents’ during the federations’ formation phase. © 2023 IEEE.

  • 20.
    Alkhabbas, Fahed
    et al.
    Malmö University, Malmo, Sweden; Malmö University, Malmo, Sweden.
    Alsadi, Mohammed
    Norwegian University Of Science And Technology, Trondheim, Norway.
    Alawadi, Sadi
    Högskolan i Halmstad, Akademin för informationsteknologi. Uppsala University, Uppsala, Sweden.
    Awaysheh, Feras M.
    University Of Tartu, Tartu, Estonia.
    Kebande, Victor R.
    Blekinge Institute Of Technology, Karlskrona, Sweden.
    Moghaddam, Mahyar T.
    University Of Southern Denmark, Odense, Denmark.
    ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems2022Inngår i: Sensors, E-ISSN 1424-8220, Vol. 22, nr 18, artikkel-id 6842Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems’ environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems’ security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach’s feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems’ constituents to learn about security threats in their environments collaboratively. © 2022 by the authors.

  • 21.
    Alonso-Fernandez, Fernando
    et al.
    ATVS/Biometric Recognition Group, Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Fierrez, Julian
    ATVS/Biometric Recognition Group, Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Fronthaler, Hartwig
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Kollreider, Klaus
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Ortega-Garcia, Javier
    ATVS/Biometric Recognition Group, Escuela Politecnica Superior, Univ. Autonoma de Madrid, Spain.
    Fingerprint Recognition2009Inngår i: Guide to Biometric Reference Systems and Performance Evaluation / [ed] Dijana Petrovska-Delacrétaz, Gérard Chollet, Bernadette Dorizzi, London: Springer London, 2009, s. 51-88Kapittel i bok, del av antologi (Annet vitenskapelig)
    Abstract [en]

    First, an overview of the state of the art in fingerprint recognition is presented, including current issues and challenges. Fingerprint databases and evaluation campaigns, are also summarized. This is followed by the description of the BioSecure Benchmarking Framework for Fingerprints, using the NIST Fingerpint Image Software (NFIS2), the publicly available MCYT-100 database, and two evaluation protocols. Two research systems are compared within the proposed framework. The evaluated systems follow different approaches for fingerprint processing and are discussed in detail. Fusion experiments involving different combinations of the presented systems are also given. The NFIS2 software is also used to obtain the fingerprint scores for the multimodal experiments conducted within the BioSecure Multimodal Evaluation Campaign(BMEC’2007) reported in Chap.11.

    Fulltekst (pdf)
    fulltext
  • 22.
    Aloulou, Hamdi
    et al.
    Institut Mines Telecom, Paris, France & Laboratory of Informatics, Robotics and Microelectronics, Montpellier, France.
    Abdulrazak, Bessam
    Laboratory of Informatics, Robotics and Microelectronics, Montpellier, France & University of Sherbrooke, Sherbrooke, Canada.
    Endelin, Romain
    Institut Mines Telecom, Paris, France & Laboratory of Informatics, Robotics and Microelectronics, Montpellier, France.
    Bentes, João
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Image and Pervasive Access Laboratory, Singapore, Singapore.
    Tiberghien, Thibaut
    Institut Mines Telecom, Paris, France & Image and Pervasive Access Laboratory, Singapore, Singapore.
    Bellmunt, Joaquim
    Institut Mines Telecom, Paris, France & Image and Pervasive Access Laboratory, Singapore, Singapore.
    Simplifying Installation and Maintenance of Ambient Intelligent Solutions Toward Large Scale Deployment2016Inngår i: Inclusive Smart Cities and Digital Health: 14th International Conference on Smart Homes and Health Telematics, ICOST 2016, Wuhan, China, May 25-27, 2016. Proceedings / [ed] Chang C.K., Jin H., Cao Y., Aloulou H., Mokhtari M., Chiari L., Heidelberg: Springer, 2016, s. 121-132Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Simplify deployment and maintenance of Ambient Intelligence solutions is important to enable large-scale deployment and maximize the use/benefit of these solutions. More mature Ambient Intelligence solutions emerge on the market as a result of an intensive investment in research. This research targets mainly the accuracy, usefulness, and usability aspects of the solutions. Still, possibility to adapt to different environments, ease of deployment and maintenance are ongoing problems of Ambient Intelligence. Existing solutions require an expert to move on-site in order to install or maintain systems. Therefore, we present in this paper our attempt to enable quick large scale deployment. We discuss lessons learned from our approach for automating the deployment process in order to be performed by ordinary people. We also introduce a solution for simplifying the monitoring and maintenance of installed systems. © Springer International Publishing Switzerland 2016.

  • 23.
    Alsaudi, Omar
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Tallozy, Yaman Mahmoud
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Developing the next generation of drones for water monitoring: Implementation of the User Interface (UI) of an internal website2022Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    This report is about implementing Graphical User Interface for the CatFish website. The CatFish project is iterative research on water pollution where samples from water bodies aremonitored and collected using three different vehicles. The authors of this report, the frontend team, have created a website that aims to represent collected data from the vehicles in the form of diagrams and charts. It also shows live video streaming and gives the CatFish team the ability to control the vehicles remotely. Our results have shown that the website is functional, user-friendly, and ready to be hosted and used.

    Fulltekst (pdf)
    fulltext
  • 24.
    Altarabichi, Mohammed Ghaith
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Fan, Yuantao
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Sheikholharam Mashhadi, Peyman
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Extracting Invariant Features for Predicting State of Health of Batteries in Hybrid Energy Buses2021Inngår i: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Porto, Portugal, 6-9 Oct., 2021, IEEE, 2021, s. 1-6Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Batteries are a safety-critical and the most expensive component for electric vehicles (EVs). To ensure the reliability of the EVs in operation, it is crucial to monitor the state of health of those batteries. Monitoring their deterioration is also relevant to the sustainability of the transport solutions, through creating an efficient strategy for utilizing the remaining capacity of the battery and its second life. Electric buses, similar to other EVs, come in many different variants, including different configurations and operating conditions. Developing new degradation models for each existing combination of settings can become challenging from different perspectives such as unavailability of failure data for novel settings, heterogeneity in data, low amount of data available for less popular configurations, and lack of sufficient engineering knowledge. Therefore, being able to automatically transfer a machine learning model to new settings is crucial. More concretely, the aim of this work is to extract features that are invariant across different settings.

    In this study, we propose an evolutionary method, called genetic algorithm for domain invariant features (GADIF), that selects a set of features to be used for training machine learning models, in such a way as to maximize the invariance across different settings. A Genetic Algorithm, with each chromosome being a binary vector signaling selection of features, is equipped with a specific fitness function encompassing both the task performance and domain shift. We contrast the performance, in migrating to unseen domains, of our method against a number of classical feature selection methods without any transfer learning mechanism. Moreover, in the experimental result section, we analyze how different features are selected under different settings. The results show that using invariant features leads to a better generalization of the machine learning models to an unseen domain.

  • 25.
    Altarabichi, Mohammed Ghaith
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Sheikholharam Mashhadi, Peyman
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Fast Genetic Algorithm for feature selection — A qualitative approximation approach2023Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 211, artikkel-id 118528Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Evolutionary Algorithms (EAs) are often challenging to apply in real-world settings since evolutionary computations involve a large number of evaluations of a typically expensive fitness function. For example, an evaluation could involve training a new machine learning model. An approximation (also known as meta-model or a surrogate) of the true function can be used in such applications to alleviate the computation cost. In this paper, we propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. We define “Approximation Usefulness” to capture the necessary conditions to ensure correctness of the EA computations when an approximation is used. Based on this definition, we propose a procedure to construct a lightweight qualitative meta-model by the active selection of data instances. We then use a meta-model to carry out the feature selection task. We apply this procedure to the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation) to create a Qualitative approXimations variant, CHCQX. We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy (as compared to CHC), particularly for large datasets with over 100K instances. We also demonstrate the applicability of the thinking behind our approach more broadly to Swarm Intelligence (SI), another branch of the Evolutionary Computation (EC) paradigm with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available. © 2022 The Author(s)

  • 26.
    Altarabichi, Mohammed Ghaith
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Sheikholharam Mashhadi, Peyman
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection2021Inngår i: 2021 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2021, s. 776-785Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Feature selection is an intractable problem, therefore practical algorithms often trade off the solution accuracy against the computation time. In this paper, we propose a novel multi-stage feature selection framework utilizing multiple levels of approximations, or surrogates. Such a framework allows for using wrapper approaches in a much more computationally efficient way, significantly increasing the quality of feature selection solutions achievable, especially on large datasets. We design and evaluate a Surrogate-Assisted Genetic Algorithm (SAGA) which utilizes this concept to guide the evolutionary search during the early phase of exploration. SAGA only switches to evaluating the original function at the final exploitation phase.

    We prove that the run-time upper bound of SAGA surrogate-assisted stage is at worse equal to the wrapper GA, and it scales better for induction algorithms of high order of complexity in number of instances. We demonstrate, using 14 datasets from the UCI ML repository, that in practice SAGA significantly reduces the computation time compared to a baseline wrapper Genetic Algorithm (GA), while converging to solutions of significantly higher accuracy. Our experiments show that SAGA can arrive at near-optimal solutions three times faster than a wrapper GA, on average. We also showcase the importance of evolution control approach designed to prevent surrogates from misleading the evolutionary search towards false optima.

  • 27.
    Altarabichi, Mohammed Ghaith
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Sheikholharam Mashhadi, Peyman
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Fast Genetic Algorithm For Feature Selection — A Qualitative Approximation Approach2023Inngår i: Evolutionary Computation Conference Companion (GECCO ’23 Companion), July 15–19, 2023, Lisbon, Portugal, New York, NY: Association for Computing Machinery (ACM), 2023, s. 11-12Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. The proposed approach involves constructing a lightweight qualitative meta-model by sub-sampling data instances and then using this meta-model to carry out the feature selection task. We define "Approximation Usefulness" to capture the necessary conditions that allow the meta-model to lead the evolutionary computations to the correct maximum of the fitness function. Based on our procedure we create CHCQX a Qualitative approXimations variant of the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation). We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy, particularly for large datasets with over 100K instances. We also demonstrate the applicability of our approach to Swarm Intelligence (SI), with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available2. This paper for the Hot-off-the-Press track at GECCO 2023 summarizes the original work published at [3].

    References

    [1] Mohammed Ghaith Altarabichi, Yuantao Fan, Sepideh Pashami, Peyman Sheikholharam Mashhadi, and Sławomir Nowaczyk. 2021. Extracting invariant features for predicting state of health of batteries in hybrid energy buses. In 2021 ieee 8th international conference on data science and advanced analytics (dsaa). IEEE, 1–6.

    [2] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2021. Surrogate-assisted genetic algorithm for wrapper feature selection. In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 776–785.

    [3] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2023. Fast Genetic Algorithm for feature selection—A qualitative approximation approach. Expert systems with applications 211 (2023), 118528.

    © 2023 Copyright held by the owner/author(s).

  • 28.
    Amirahmadi, Ali
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Lund University, Lund, Sweden.
    Etminani, Kobra
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Melander, Olle
    Lund University, Lund, Sweden.
    Björk, Jonas
    Lund University, Lund, Sweden.
    A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning2023Inngår i: Caring is Sharing – Exploiting the Value in Data for Health and Innovation: Proceedings of MIE 2023 / [ed] Maria Hägglund; Madeleine Blusi; Stefano Bonacina; Lina Nilsson; Inge Cort Madsen; Sylvia Pelayo; Anne Moen; Arriel Benis; Lars Lindsköld; Parisis Gallos, Amsterdam: IOS Press, 2023, Vol. 302, s. 609-610Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes). © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.

  • 29.
    Amoozegar, Maryam
    et al.
    School of Computer Engineering, Iran University of Science and Technology, Narmak, Tehran, 1684613114, Iran.
    Minaei-Bidgoli, Behrouz
    School of Computer Engineering, Iran University of Science and Technology, Narmak, Tehran, 1684613114, Iran.
    Mansoor, Rezghi
    Department of Computer Science, Tarbiat Modares University, Tehran, 14115-175, Iran.
    Fanaee Tork, Hadi
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Extra-adaptive robust online subspace tracker for anomaly detection from streaming networks2020Inngår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 94, artikkel-id 103741Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Anomaly detection in time-evolving networks has many applications, for instance, traffic analysis in transportation networks and intrusion detection in computer networks. One group of popular methods for anomaly detection from evolving networks are robust online subspace trackers. However, these methods suffer from problem of insensitivity to drastic changes in the evolving subspace. In order to solve this problem, we propose a new robust online subspace and anomaly tracker, which is more adaptive and robust against sudden drastic changes in the subspace. More accurate estimation of low rank and sparse components by this tracker leads to more accurate anomaly detection. We evaluate the accuracy of our method with real-world dynamic network data sets with varying sparsity levels. The result is promising and our method outperforms the state-of-the-art.

  • 30.
    Andersson, Magnus
    et al.
    Telematics Group, Viktoria Institute, Göteborg.
    Lindgren, Rikard
    Telematics Group, Viktoria Institute, Göteborg.
    The Mobile-Stationary Divide in Ubiquitous Computing Environments: Lessons from the Transport Industry2005Inngår i: Information systems management, ISSN 1058-0530, E-ISSN 1934-8703, Vol. 22, nr 4, s. 65-79Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The emergence of ubiquitous computing offers new possibilities and opportunities for organizations attempting to improve their productivity and effectiveness. In particular, the promises of ubiquitous computing are attractive to organizations such as transport firms, in which coordination of diverse sets of mobile units is central to organizational performance. This article analyzes the use of ubiquitous transport systems in Swedish road haulage firms and discusses the opportunities and challenges for the early adopters. It pays specific attention to the mobile-stationary divide; that is, the set of challenges associated with integration of mobile and stationary people and systems into a seamless computing environment.

  • 31.
    Andersson, Magnus
    et al.
    Telematics Group, Viktoria Institute, Göteborg, Sweden.
    Lindgren, Rikard
    Telematics Group, Viktoria Institute, Göteborg, Sweden.
    Henfridsson, Ola
    Telematics Group, Viktoria Institute, Göteborg, Sweden.
    Assessing the Mobile-Stationary Divide in Ubiquitous Transport Systems2005Inngår i: Designing Ubiquitous Information Environments: Socio-Technical Issues and Challenges, New York, USA: Springer-Verlag New York, 2005, s. 123-137Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Many transport organizations seek to develop seamlessly integrated computing environments. A central problem in attempts to realize such ubiquitous transport systems is the divide that exists between stationary transport management systems and mobile applications such as embedded vehicle sensor networks and in-vehicle services for message handling. Originating from different Innovation regimes, these technologies are heterogeneous in that they rely on different technological platforms and knowledge bases, as well as the institutionalized settings from which they have emerged. This paper assesses how the mobile-stationary divide plays out in practical efforts to develop ubiquitous transport systems in road haulage firms. This assessment is conducted through a multiple-case study that identifies socio-technical challenges associated with this divide. Building on this assessment, the paper contributes a set of implications for enterprise-wide ubiquitous computing environments where coordination of diverse sets of mobile units is central to organizational performance. On a general level, these implications are important for any organization attempting to integrate mobile and stationary information systems.

  • 32.
    Andersson, Oscar
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Andersson, Tim
    Högskolan i Halmstad, Akademin för informationsteknologi.
    AI applications on healthcare data2021Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    The purpose of this research is to get a better understanding of how different machine learning algorithms work with different amounts of data corruption. This is important since data corruption is an overbearing issue within data collection and thus, in extension, any work that relies on the collected data. The questions we were looking at were: What feature is the most important? How significant is the correlation of features? What algorithms should be used given the data available? And, How much noise (inaccurate or unhelpful captured data) is acceptable? 

    The study is structured to introduce AI in healthcare, data missingness, and the machine learning algorithms we used in the study. In the method section, we give a recommended workflow for handling data with machine learning in mind.

    The results show us that when a dataset is filled with random values, the run-time of algorithms increases since many patterns are lost. Randomly removing values also caused less of a problem than first anticipated since we ran multiple trials, evening out any problems caused by the lost values. Lastly, imputation is a preferred way of handling missing data since it retained many dataset structures. One has to keep in mind if the imputation is done on categories or numerical values.

    However, there is no easy "best-fit" for any dataset. It is hard to give a concrete answer when choosing a machine learning algorithm that fits any dataset. Nevertheless, since it is easy to simply plug-and-play with many algorithms, we would recommend any user try different ones before deciding which one fits a project the best.

    Fulltekst (pdf)
    fulltext
  • 33.
    Aramrattana, Maytheewat
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES). The Swedish National Road and Transport Research Institute (VTI), Göteborg, Sweden.
    A Simulation-Based Safety Analysis of CACC-Enabled Highway Platooning2018Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Cooperative Intelligent Transport Systems (C-ITS) enable actors in the transport systems to interact and collaborate by exchanging information via wireless communication networks. There are several challenges to overcome before they can be implemented and deployed on public roads. Among the most important challenges are testing and evaluation in order to ensure the safety of C-ITS applications.

    This thesis focuses on testing and evaluation of C-ITS applications with regard to their safety using simulation. The main focus is on one C-ITS application, namely platooning, that is enabled by the Cooperative Adaptive Cruise Control (CACC) function. Therefore, this thesis considers two main topics: i) what should be modelled and simulated for testing and evaluation of C-ITS applications? and ii) how should CACC functions be evaluated in order to ensure safety?

    When C-ITS applications are deployed, we can expect traffic situations which consist of vehicles with different capabilities, in terms of automation and connectivity. We propose that involving human drivers in testing and evaluation is important in such mixed traffic situations. Considering important aspects of C-ITS including human drivers, we propose a simulation framework, which combines driving-, network-, and traffic simulators. The simulation framework has been validated by demonstrating several use cases in the scope of platooning. In particular, it is used to demonstrate and analyse the safety of platooning applications in cut-in situations, where a vehicle driven by a human driver cuts in between vehicles in platoon. To assess the situations, time-to-collision (TTC) and its extensions are used as safety indicators in the analyses.

    The simulation framework permits future C-ITS research in other fields such as human factors by involving human drivers in a C-ITS context. Results from the safety analyses show that cut-in situations are not always hazardous, and two factors that are the most highly correlated to the collisions are relative speed and distance between vehicles at the moment of cutting in. Moreover, we suggest that to solely rely on CACC functions is not sufficient to handle cut-in situations. Therefore, guidelines and standards are required to address these situations properly.

    Fulltekst (pdf)
    fulltext
  • 34.
    Aramrattana, Maytheewat
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES). The Swedish National Road and Transport Research Institute (VTI), Göteborg, Sweden.
    Englund, Cristofer
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). RISE Viktoria, Göteborg, Sweden.
    Larsson, Tony
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Jansson, Jonas
    The Swedish National Road and Transport Research Institute (VTI), Linköping, Sweden.
    Nåbo, Arne
    The Swedish National Road and Transport Research Institute (VTI), Göteborg, Sweden.
    Safety Evaluation of Highway Platooning Under a Cut-In Situation Using Simulation2018Inngår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Platooning refers to an application, where a group of connected and automated vehicles follow a lead vehicle autonomously, with short inter-vehicular distances. At merging points on highways such as on-ramp, platoons could encounter manually driven vehicles, which are merging on to the highways. In some situations, the manually driven vehicles could end up between the platooning vehicles. Such situations are expected and known as “cut-in” situations. This paper presents a simulation study of a cut-in situation, where a platoon of five vehicles encounter a manually driven vehicle at a merging point of a highway. The manually driven vehicle is driven by 37 test persons using a driving simulator. For the platooning vehicles, two longitudinal controllers with four gap settings between the platooning vehicles, i.e. 15 meters, 22.5 meters, 30 meters, and 42.5 meters, are evaluated. Results summarizing cut-in behaviours and how the participants perceived the situation are presented. Furthermore, the situation is assessed using safety indicators based on time-to-collision.

  • 35.
    Artmann, Daniel
    Högskolan i Halmstad.
    Applying machine learning algorithms to multi-label text classification on GitHub issues2020Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    This report compares five machine learning algorithms in their ability to categorize code repositories. The focus of expanding software projects tend to shift from developing new software to the maintenance of the projects. Maintainers can label code repositories to organize the project, but this requires manual labor and time. This report will evaluate how machine learning algorithms perform in automatically classifying code repositories. Automatic classification can aid the management process by reducing both manual labor and human errors.

    GitHub provides online hosting for both private and public code repositories. In these repositories, users can open issues and assign labels to them, to keep track of bugs, enhancement, or requests. GitHub was used as a source for all data as it contains millions of open-source repositories. The focus was on the most popular labels from GitHub - both default labels and those defined by users.

    This report investigated the algorithms linear regression (LR), convolutional neural network (CNN), recurrent neural network (RNN), random forest (RF), and k-nearest-neighbor (KNN) - in multi-label text classification. The mentioned algorithms were implemented, trained, and tested with the Keras and Scikit-learn libraries. The training sets contained around 38 thousand rows and the test set around 12 thousand rows. Cross-validation was used to measure the performance of each algorithm. The metrics used to obtain the results were precision, recall, and F1-score. The algorithms were empirically tested on a different number of output labels. In order to maximize the F1-score, different designs of the neural networks and different natural language processing (NLP) methods were evaluated. This was done to see if the algorithms could be used to efficiently organize code repositories.

    CNN displayed the best scores in all experiments, but LR, RNN, and RF also showed some good results. LR, CNN, and RNN the had the highest F1-scores while RF could achieve a particularly high precision. KNN performed much worse than all other algorithms. The highest F1-score of 46.48% was achieved when using a non-sequential CNN model that used text input with stem words. The highest precision of 89.17% was achieved by RF.

    It was concluded that LR, CNN, RNN, and RF were all viable in classifying labels in software-related texts, among those found in GitHub issues. KNN wasn't found to be a viable candidate for this purpose.

    Fulltekst (pdf)
    fulltext
  • 36.
    Ashfaq, Awais
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Deep Evidential Doctor2022Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    Recent years have witnessed an unparalleled surge in deep neural networks (DNNs) research, surpassing traditional machine learning (ML) and statistical methods on benchmark datasets in computer vision, audio processing and natural language processing (NLP). Much of this success can be attributed to the availability of numerous open-source datasets, advanced computational resources and algorithms. These algorithms learn multiple levels of simple to complex abstractions (or representations) of data resulting in superior performances on downstream applications. This has led to an increasing interest in reaping the potential of DNNs in real-life safety-critical domains such as autonomous driving, security systems and healthcare. Each of them comes with their own set of complexities and requirements, thereby necessitating the development of new approaches to address domain-specific problems, even if building on common foundations.

    In this thesis, we address data science related challenges involved in learning effective prediction models from structured electronic health records (EHRs). In particular, questions related to numerical representation of complex and heterogeneous clinical concepts, modelling the sequential structure of EHRs and quantifying prediction uncertainties are studied. From a clinical perspective, the question of predicting onset of adverse outcomes for individual patients is considered to enable early interventions, improve patient outcomes, curb unnecessary expenditures and expand clinical knowledge.

    This is a compilation thesis including five articles. It begins by describing a healthcare information platform that encapsulates clinical, operational and financial data of patients across all public care delivery units in Halland, Sweden. Thus, the platform overcomes the technical and legislative data-related challenges inherent to the modern era's complex and fragmented healthcare sector. The thesis presents evidence that expert clinical features are powerful predictors of adverse patient outcomes. However, they are well complemented by clinical concept embeddings; gleaned via NLP inspired language models. In particular, a novel representation learning framework (KAFE: Knowledge And Frequency adapted Embeddings) has been proposed that leverages medical knowledge schema and adversarial principles to learn high quality embeddings of both frequent and rare clinical concepts. In the context of sequential EHR modelling, benchmark experiments on cost-sensitive recurrent nets have shown significant improvements compared to non-sequential networks. In particular, an attention based hierarchical recurrent net is proposed that represents individual patients as weighted sums of ordered visits, where visits are, in turn, represented as weighted sums of unordered clinical concepts. In the context of uncertainty quantification and building trust in models, the field of deep evidential learning has been extended. In particular for multi-label tasks, simple extensions to current neural network architecture are proposed, coupled with a novel loss criterion to infer prediction uncertainties without compromising on accuracy. Moreover, a qualitative assessment of the model behaviour has also been an important part of the research articles, to analyse the correlations learned by the model in relation to established clinical science.

    Put together, we develop DEep Evidential Doctor (DEED). DEED is a generic predictive model that learns efficient representations of patients and clinical concepts from EHRs and quantifies its confidence in individual predictions. It is also equipped to infer unseen labels.

    Overall, this thesis presents a few small steps towards solving the bigger goal of artificial intelligence (AI) in healthcare. The research has consistently shown impressive prediction performance for multiple adverse outcomes. However, we believe that there are numerous emerging challenges to be addressed in order to reap the full benefits of data and AI in healthcare. For future works, we aim to extend the DEED framework to incorporate wider data modalities such as clinical notes, signals and daily lifestyle information. We will also work to equip DEED with explainability features.

    Fulltekst (pdf)
    fulltext
  • 37.
    Ashfaq, Awais
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi. Halland Hospital, Halmstad, Sweden.
    Lingman, Markus
    Högskolan i Halmstad, Akademin för informationsteknologi. Halland Hospital, Halmstad, Sweden; Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Sensoy, Murat
    Amazon, Seattle, United States.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi.
    DEED: DEep Evidential Doctor2023Inngår i: Artificial Intelligence, ISSN 0004-3702, E-ISSN 1872-7921, Vol. 325, artikkel-id 104019Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    As Deep Neural Networks (DNN) make their way into safety-critical decision processes, it becomes imperative to have robust and reliable uncertainty estimates for their predictions for both in-distribution and out-of-distribution (OOD) examples. This is particularly important in real-life high-risk settings such as healthcare, where OOD examples (e.g., patients with previously unseen or rare labels, i.e., diagnoses) are frequent, and an incorrect clinical decision might put human life in danger, in addition to having severe ethical and financial costs. While evidential uncertainty estimates for deep learning have been studied for multi-class problems, research in multi-label settings remains untapped. In this paper, we propose a DEep Evidential Doctor (DEED), which is a novel deterministic approach to estimate multi-label targets along with uncertainty. We achieve this by placing evidential priors over the original likelihood functions and directly estimating the parameters of the evidential distribution using a novel loss function. Additionally, we build a redundancy layer (particularly for high uncertainty and OOD examples) to minimize the risk associated with erroneous decisions based on dubious predictions. We achieve this by learning the mapping between the evidential space and a continuous semantic label embedding space via a recurrent decoder. Thereby inferring, even in the case of OOD examples, reasonably close predictions to avoid catastrophic consequences. We demonstrate the effectiveness of DEED on a digit classification task based on a modified multi-label MNIST dataset, and further evaluate it on a diagnosis prediction task from a real-life electronic health record dataset. We highlight that in terms of prediction scores, our approach is on par with the existing state-of-the-art having a clear advantage of generating reliable, memory and time-efficient uncertainty estimates with minimal changes to any multi-label DNN classifier. © 2023 The Author(s)

  • 38.
    Assabie, Yaregal
    et al.
    Addis Ababa University, Department of Computer Science, Addis Ababa Ethiopia .
    Bigun, Josef
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Lexicon-based Offline Recognition of Amharic Words in Unconstrained Handwritten Text2008Inngår i: 19th International Conference on Pattern Recognition: (ICPR 2008) ; Tampa, Florida, USA 8-11 December 2008, New York: IEEE Computer Society, 2008, artikkel-id 4761145Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper describes an offline handwriting recognition system for Amharic words based on lexicon. The system computes direction fields of scanned handwritten documents, from which pseudo-characters are segmented. The pseudo-characters are organized based on their proximity and direction to form text lines. Words are then segmented by analyzing the relative gap between subsequent pseudocharacters in text lines. For each segmented word image, the structural characteristics of pseudo-characters are syntactically analyzed to predict a set of plausible characters forming the word. The most likelihood word is finally selected among candidates by matching against the lexicon. The system is tested by a database of unconstrained handwritten Amharic documents collected from various sources. The lexicon is prepared from words appearing in the collected database.

  • 39.
    Bacauskiene, Marija
    et al.
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Verikas, Antanas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Gelzinis, Adas
    Department of Electrical and Control Equipment, Kaunas University of Technology, Kaunas, Lithuania.
    Vegiene, Aurelija
    Department of Otolaryngology, Kaunas University of Medicine, Kaunas, Lithuania.
    Random forests based monitoring of human larynx using questionnaire data2012Inngår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 39, nr 5, s. 5506-5512Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper is concerned with soft computing techniques-based noninvasive monitoring of human larynx using subject’s questionnaire data. By applying random forests (RF), questionnaire data are categorized into a healthy class and several classes of disorders including: cancerous, noncancerous, diffuse, nodular, paralysis, and an overall pathological class. The most important questionnaire statements are determined using RF variable importance evaluations. To explore data represented by variables used by RF, the t-distributed stochastic neighbor embedding (t-SNE) and the multidimensional scaling (MDS) are applied to the RF data proximity matrix. When testing the developed tools on a set of data collected from 109 subjects, the 100% classification accuracy was obtained on unseen data in binary classification into the healthy and pathological classes. The accuracy of 80.7% was achieved when classifying the data into the healthy, cancerous, noncancerous classes. The t-SNE and MDS mapping techniques applied allow obtaining two-dimensional maps of data and facilitate data exploration aimed at identifying subjects belonging to a “risk group”. It is expected that the developed tools will be of great help in preventive health care in laryngology.

  • 40.
    Barisas, Dominykas
    et al.
    Kaunas University of Technology, Kaunas, Lithuania.
    Duracz, Adam
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Taha, Walid
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES). Rice University, Houston, TX, USA.
    DSLs Should be Online Applications2014Inngår i: Joint International Conference on Engineering Education & International Conference on Information Technology: ICEE/ICIT-2014: June 2 - 6, 2014: Riga, Latvia: Conference proceedings, 2014, s. 314-319Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Domain-Specific Languages (DSLs) play an important role in both practice and education. But developing them is challenging, because a DSL must ultimately satisfy a large and complex set of user/customer requirements to fulfil its intended role, and neither requirements nor users are fully available at all times during the development process. Requirements can be elicited using agile methods but such methods assume the availability of the users. The situation is further complicated when the user base is primarily students and when enhanced learning is a key requirement. In this paper we propose developing DSLs, especially educational ones, as online applications. We analyze how this can help requirement elicitation and learning. Being online brings language development closer to the user, yielding new opportunities to improve and accelerate the language design process. It is also well-matched to agile methods, since web- based analytics provide an abundant source of data that integrates naturally into the development process. As an example, we consider applying the method to Acumen, a DSL designed to support teaching Cyber-Physical Systems.

  • 41.
    Bengtsson, Jerker
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Baseband Processing in 3G UMTS Radio Base Stations2006Rapport (Annet vitenskapelig)
    Abstract [en]

    This report presents a study of functionality, service dataflows, computation characteristics and processing parameters for baseband processing in radio base stations. The study has been performed with the objective to develop a programming model that is natural and efficient to use for baseband programming and which can be efficiently compiled to parallel computing structures. In order to achieve this objective it is necessary to analyse and understand the logical architecture of the application in order to be able to define processing characteristics and thereby requirements on languages as well as on physical system architectures. Moreover, to be able to test and verify programming and mapping of functions it is necessary to have realistic but still manageable test cases. The study is focused on the third generation partnership project (3GPP) standard specifications for 3G radio base stations. The specifications cover the complete 3G network-architecture and are quite extensive and complex. To make experiments manageable, it is necessary to abstract system functionality that is not directly relevant for the RBS baseband processing. Moreover, the standard specifications only describe the required processing functionality on an abstract logical level. In this report, the functionality of the baseband functions is explained and also described using illustrations of dataflows and abstract mapping of two 3G service cases. The results of the study constitute a comprehensive description of the processing flow and the mapping of user data channels in 3G radio base stations – spanning data and control input from layer 2 to physical channel output from layer 1. Data dependencies between functions are illustrated with figures and it is concluded that these dependencies are of producer/consumer type. It is discussed how different functions can be mapped in MIMD and SIMD fashion with regard to the data dependencies, the data stream lengths and the control operations required to handle bit stream processing on word-length processor architectures.

    Fulltekst (pdf)
    FULLTEXT01
  • 42.
    Bengtsson, Jerker
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Hoang Bengtsson, Hoai
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Dynamic Real-time DSP on Manycores2010Konferansepaper (Fagfellevurdert)
  • 43.
    Bengtsson, Jerker
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS).
    Svensson, Bertil
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    A configurable framework for stream programming exploration in baseband applications2006Inngår i: 2006 IEEE International Parallel & Distributed Processing Symposium: Rhodes Island, Greece : 25-29 April, 2006, Piscataway, N.J.: IEEE Press, 2006, s. 8-Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents a configurable framework to be used for rapid prototyping of stream based languages. The framework is based on a set of design patterns defining the elementary structure of a domain specific language for high-performance signal processing. A stream language prototype for baseband processing has been implemented using the framework. We introduce language constructs to efficiently handle dynamic reconfiguration of distributed processing parameters. It is also demonstrated how new language specific primitive data types and operators can be used to efficiently and machine independently express computations on bitfields and data-parallel vectors. These types and operators yield code that is readable, compact and amenable to a stricter type checking than is common practice. They make it possible for a programmer to explicitly express parallelism to be exploited by a compiler. In short, they provide a programming style that is less error prone and has the potential to lead to more efficient implementations.

    Fulltekst (pdf)
    FULLTEXT01
  • 44.
    Bengtsson, Lars
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS). Chalmers University of Technology, Gothenburg, Sweden.
    Linde, Arne
    Chalmers University of Technology, Gothenburg, Sweden.
    Nordström, Tomas
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS). Telecommunications Research Center Vienna (FTW), Vienna, Austria.
    Svensson, Bertil
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Taveniku, Mikael
    XCube Communication, Inc., Westford, MA, United States.
    The REMAP Reconfigurable Architecture: a Retrospective2006Inngår i: FPGA Implementations of Neural Networks, New York: Springer-Verlag New York, 2006, s. 325-360Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    The goal of the REMAP project was to gain new knowledge about the design and use of massively parallel computer architectures in embedded real-time systems. In order to support adaptive and learning behavior in such systems, the efficient execution of Artificial Neural Network (ANN) algorithms on regular processor arrays was in focus. The REMAP-β parallel computer built in the project was designed with ANN computations as the main target application area. This chapter gives an overview of the computational requirements found in ANN algorithms in general and motivates the use of regular processor arrays for the efficient execution of such algorithms. REMAP-β was implemented using the FPGA circuits that were available around 1990. The architecture, following the SIMD principle (Single Instruction stream, Multiple Data streams), is described, as well as the mapping of some important and representative ANN algorithms. Implemented in FPGA, the system served as an architecture laboratory. Variations of the architecture are discussed, as well as scalability of fully synchronous SIMD architectures. The design principles of a VLSI-implemented successor of REMAP-β are described, and the paper is concluded with a discussion of how the more powerful FPGA circuits of today could be used in a similar architecture. © 2006 Springer.

  • 45.
    Beohar, Harsh
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Cuijpers, Pieter
    Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.
    Avoiding Diamonds in Desynchronisation2014Inngår i: Science of Computer Programming, ISSN 0167-6423, E-ISSN 1872-7964, Vol. 91, nr PART A, s. 45-69Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The design of concurrent systems often assumes synchronous communication between different parts of a system. When system components are physically apart, this assumption becomes inappropriate. Desynchronisation is a technique that aims to implement a synchronous design in an asynchronous manner by placing buffers between the components of the synchronous design. When queues are used as buffers, the so-called 'diamond property' (among others) ensures correct operation of the desynchronised design. However, this property is difficult to establish in practice. In this paper, we give sufficient and necessary conditions under which a concrete synchronous design (i.e., without the unobservable action) is equivalent to an asynchronous design and formally prove that the diamond property is no longer needed for desynchronisation when half-duplex queues are used as a communication buffer. Furthermore, we discuss how the half-duplex condition can be further relaxed when the diamond property can be partially guaranteed. To illustrate how this theory may be applied, we desynchronise the synchronous systems that are synthesised using supervisory control theory. © 2013 Elsevier B.V.

    Fulltekst (pdf)
    hb-scp-2014
  • 46.
    Beohar, Harsh
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Mousavi, Mohammad Reza
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    A Pre-congruence Format for XY-simulation2015Inngår i: Fundamentals of Software Engineering: 6th International Conference, FSEN 2015 Tehran, Iran, April 22–24, 2015, Revised Selected Papers / [ed] Mehdi Dastani & Marjan Sirjani, Cham: Springer, 2015, Vol. 9392, s. 215-229Konferansepaper (Fagfellevurdert)
    Abstract [en]

    XY-simulation is a generalization of bisimulation that is parameterized with two subsets of actions. XY-simulation is known in the literature under different names such as modal refinement, partial bisimulation, and alternating simulation. In this paper, we propose a precongruence rule format for XY-simulation. The format allows for checking compositionality of XY-simulation for an arbitrary language with structural operational semantics, by performing very simple checks on the syntactic shape of the rules. We apply our format to derive concrete compositionality results for different notions of behavioral pre-order with respect to different process calculi in the literature. © IFIP International Federation for Information Processing 2015

    Fulltekst (pdf)
    fulltext
  • 47.
    Beohar, Harsh
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Varshosaz, Mahsa
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Mousavi, Mohammad Reza
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Centrum för forskning om inbyggda system (CERES).
    Basic behavioral models for software product lines: Expressiveness and testing pre-orders2016Inngår i: Science of Computer Programming, ISSN 0167-6423, E-ISSN 1872-7964, Vol. 123, s. 42-60Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In order to provide a rigorous foundation for Software Product Lines (SPLs), several fundamental approaches have been proposed to their formal behavioral modeling. In this paper, we provide a structured overview of those formalisms based on labeled transition systems and compare their expressiveness in terms of the set of products they can specify. Moreover, we define the notion of tests for each of these formalisms and show that our notions of testing precisely capture product derivation, i.e., all valid products will pass the set of test cases of the product line and each invalid product fails at least one test case of the product line. © 2015 The Authors.

    Fulltekst (pdf)
    fulltext
  • 48.
    Bepari, Nuruzzaman
    et al.
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Alam, Mohammad Zabedul
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Rahman, Md. Syadur
    Högskolan i Halmstad, Sektionen för Informationsvetenskap, Data– och Elektroteknik (IDE).
    Architecture of a Distributed LDAP Directory2010Independent thesis Advanced level (degree of Master (One Year)), 15 poäng / 22,5 hpOppgave
    Fulltekst (pdf)
    FULLTEXT01
  • 49.
    Berenji, Amirhossein
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Taghiyarrenani, Zahra
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Data-Centric Perspective on Explainability Versus Performance Trade-Off2023Inngår i: Advances in Intelligent Data Analysis XXI: 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings / [ed] Bruno Crémilleux, Sibylle Hess, Siegfried Nijssen, Cham: Springer, 2023, Vol. 13876, s. 42-54Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The performance versus interpretability trade-off has been well-established in the literature for many years in the context of machine learning models. This paper demonstrates its twin, namely the data-centric performance versus interpretability trade-off. In a case study of bearing fault diagnosis, we found that substituting the original acceleration signal with a demodulated version offers a higher level of interpretability, but it comes at the cost of significantly lower classification performance. We demonstrate these results on two different datasets and across four different machine learning algorithms. Our results suggest that “there is no free lunch,” i.e., the contradictory relationship between interpretability and performance should be considered earlier in the analysis process than it is typically done in the literature today; in other words, already in the preprocessing and feature extraction step. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

    Fulltekst (pdf)
    fulltext
  • 50.
    Berenji, Amirhossein
    et al.
    Shahid Beheshti University, Tehran, Iran.
    Taghiyarrenani, Zahra
    Högskolan i Halmstad, Akademin för informationsteknologi.
    An Analysis of Vibrations and Currents for Broken Rotor Bar Detection in Three-phase Induction Motors2022Inngår i: Proceedings of the European Conference of the Prognostics and Health Management Society 2022 / [ed] Phuc Do; Gabriel Michau; Cordelia Ezhilarasu, State College, PA: PHM Society , 2022, Vol. 7 (1), s. 43-48Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Selecting the physical property capable of representing the health state of a machine is an important step in designing fault detection systems. In addition, variation of the loading condition is a challenge in deploying an industrial predictive maintenance solution. The robustness of the physical properties to variations in loading conditions is, therefore, an important consideration. In this paper, we focus specifically on squirrel cage induction motors and analyze the capabilities of three-phase current and five vibration signals acquired from different locations of the motor for the detection of Broken Rotor Bar generated in different loads. In particular, we examine the mentioned signals in relation to the performance of classifiers trained with them. Regarding the classifiers, we employ deep conventional classifiers and also propose a hybrid classifier that utilizes contrastive loss in order to mitigate the effect of different variations. The analysis shows that vibration signals are more robust under varying load conditions. Furthermore, the proposed hybrid classifier outperforms conventional classifiers and is able to achieve an accuracy of 90.96% when using current signals and 97.69% when using vibration signals.

    Fulltekst (pdf)
    fulltext
1234567 1 - 50 of 384
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