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  • 1.
    Andersdotter, Emma
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Säkerhet i molntjänster: Starka autentiseringsmetoder, federeringslösningar & säkerhetshot mot autentisering i moln2014Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    "Moln" är ett känt ord använt för att beskriva molntjänster. Begreppet moln uppstod för längesen, och användes bland annat på 70-talet för att representera delar som ingen riktigt förstod sig på. Det är inte bara ordet moln som fortfarande orsakar förvirring, utan även dess flertaliga beskrivningar. Dessa gör det svårt att få grepp om vad molntjänster egentligen innebär. Kraven för molntjänster är flera, men man talar oftast om bred tillgänglighet, konsolidering av resurser, snabb elasticitet och mätbara, självbetjäningstjänster. Implementering av säkerhet är viktig för molntjänster för att kunna skydda dem från säkerhetshot. En vanlig men också viktig säkerhetskontroll är autentisering. Autentisering tillsammans med federationslösningar möjliggör för integration mellan molntjänster som vill dela autentisering.

    Rapporten är strukturerad i olika sektioner, innehållande bakgrund till problem, syften som leder fram till frågeställningar och vald metodik. Den teoretiska biten består av en litteraturstudie om bland annat; säkerhetshot mot autentisering i molnet; skydd mot dessa; autentiseringsmetoder passande för molnet; federationslösningar. I analysen jämför rapporten skillnader och likheter mellan resultat från litteraturstudien och från flera intervjuer. I slutsatsen presenteras svar på frågeställningar och resultat från analysdelen. Integration mellan molntjänsters autentiseringslösning kan lösas av federerade identiteter, som även medför fördelarna användarvänlighet och enklare administration. Att alla identiteter samlas på en och samma plats kan dock bli ett attraktivt mål för attacker. Säkerhetshot mot molntjänster existerar i all högsta grad, dock skiljer de sig inte mycket mot traditionella tjänster. Slutsatsen påvisar att kausalitet fortfarande råder för säkerhet i molntjänster, men att förtroende har blivit ytterligare en förklaringsvariabel.

  • 2.
    Berg, Leonhard
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Montelius, Olle
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Barnsäkring av Smartphones2018Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Genom detta arbete demonstreras hur man, genom att kombinera tillgängliga gratistjänster, samt med lite egen kod, kan åstadkomma en prototyp av ett säkert och barnanpassat heltäckande system för användande av Android-enheter. Internettrafik från den mobila enheten dirigeras genom VPN-tunnel till hemmanätverket vilket ger minskad attackyta och minskad avlyssningsrisk, och genom användning av DNS-filtrering kan olämpliga webbsidor på förhand blockeras. Enheten kan användas för samtal (och med ytterligare modifiering SMS) men inkommande samtal från icke-godkända avsändarnummer, samt SMS blockeras. Genom Applocker, en sorts låsmekanism för applikationsåtkomst, förhindras olämpliga applikationer att installeras eller användas på enheten, likväl som inställningar förhindras att ändras av annan än administratör. Genom användande av en App-launcher kan begränsningen stramas åt ytterligare.

    Den sammantagna lösningen renderar i ett system där en Smartphone på ett fördelaktigt sätt kan handhas av barn och där utvalda funktioner går att använda på vanligt sätt men där, i förväg spärrade, funktioner har eliminerats från enheten.

    Vad som krävs utöver en Smartphone, är ett fungerande hemmanätverk och en enklare - alltid påslagen - ”server”-dator, förslagsvis en Raspberry Pi.

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  • 3.
    Bergstrand, Daniel
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Analys av kvarvarande spår från USB-lagringsenheter2015Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Att utföra en polisiär IT-forensisk undersökning kan ibland påminna om att lägga ett pussel, specielltom personen som ska utföra undersökningen inte var med när den initiala bevissäkringen gjordes. Attbilda sig en uppfattning om hur en dator har använts enbart utifrån innehållet på dess hårddisk kanvara en svår och många gånger tidskrävande uppgift. Situationer som kan ses som specielltkomplicerade är när potentiellt bevisbärande komponenter saknas, när man saknar pusselbitar.Vid en undersökning av en dators hårddisk påträffas ofta spår av olika enheter som har anslutits tilldatorn. Spår av flyttbara enheter kan många gånger vara intressanta vid en IT-forensisk undersökning.Det kan röra sig om att påvisa hur olika enheter har använts i förhållande till varandra, ibland till ochmed knyta ett digitalt spår till en fysisk handling. Vid andra tillfällen kan spår av tidigare anslutnaenheter berätta för undersökaren att denne inte har tillgång till alla enheter som kan vara av intresse förundersökningen.Arbetet beskriver de mål som är eftersträvansvärda vid uppbyggnaden av IT-forensiskaundersökningsmetoder. Flera aspekter vägdes in för att arbetet skulle hålla en för polisäraundersökningsmetoder tillräcklig kvalitet.Examensarbetet kretsar till stor del kring det testutförande som genomfördes genom att ansluta ettUSB-minne till en dator. Datorn undersöktes sedan efter förändringar och de filer som identifieradessom intressanta bearbetades med ändamålsenliga verktyg. Resultaten som lämnades av verktygengranskades, antingen med sekundära verktyg eller med manuella verifieringsmetoder.Spåren från undersökningen gav vid handen en metod för hur en IT-forensiker verksam i polisiärtutredningsarbete kan angripa spår lämnade av flyttbara USB-lagringsenheter. Slutligen granskadesmetoden och slutsatser kring dess styrkor och brister drogs.

  • 4.
    Bohm, Clifford
    et al.
    Michigan State University, Department of Integrative Biology and BEACON Center for the Study of Evolution in Action, East Lansing, U.S.A..
    Kirkpatrick, Douglas
    Michigan State University, BEACON Center for the Study of Evolution in Action and Department of Computer Science and Engineering, East Lansing, U.S.A.
    Hintze, Arend
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Michigan State University, BEACON Center for the Study of Evolution in Action, East Lansing, U.S.A..
    Understanding Memories of the Past in the Context of Different Complex Neural Network Architectures.2022Ingår i: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 34, nr 3, s. 754-780Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Deep learning (primarily using backpropagation) and neuroevolution are the preeminent methods of optimizing artificial neural networks. However, they often create black boxes that are as hard to understand as the natural brains they seek to mimic. Previous work has identified an information-theoretic tool, referred to as R, which allows us to quantify and identify mental representations in artificial cognitive systems. The use of such measures has allowed us to make previous black boxes more transparent. Here we extend R to not only identify where complex computational systems store memory about their environment but also to differentiate between different time points in the past. We show how this extended measure can identify the location of memory related to past experiences in neural networks optimized by deep learning as well as a genetic algorithm.

  • 5.
    Carling, Kenneth
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Meng, Xiangli
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    On statistical bounds of heuristic solutions to location problems2014Rapport (Övrigt vetenskapligt)
    Abstract [en]

    Solutions to combinatorial optimization problems, such as problems of locating facilities, frequently rely on heuristics to minimize the objective function. The optimum is sought iteratively and a criterion is needed to decide when the procedure (almost) attains it. Pre-setting the number of iterations dominates in OR applications, which implies that the quality of the solution cannot be ascertained. A small, almost dormant, branch of the literature suggests using statistical principles to estimate the minimum and its bounds as a tool to decide upon stopping and evaluating the quality of the solution. In this paper we examine the functioning of statistical bounds obtained from four different estimators by using simulated annealing on p-median test problems taken from Beasley’s OR-library. We find the Weibull estimator and the 2nd order Jackknife estimator preferable and the requirement of sample size to be about 10 being much less than the current recommendation. However, reliable statistical bounds are found to depend critically on a sample of heuristic solutions of high quality and we give a simple statistic useful for checking the quality. We end the paper with an illustration on using statistical bounds in a problem of locating some 70 distribution centers of the Swedish Post in one Swedish region. 

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    On statistical bounds of heuristic solutions
  • 6. Chen, Jifu
    et al.
    Mao, Chengying
    Song, William Wei
    Högskolan Dalarna, Institutionen för information och teknik, Informatik. School of Software and IoT Engineering, Jiangxi University of Finance and Economics, China.
    QoS prediction for web services in cloud environments based on swarm intelligence search2023Ingår i: Knowledge-Based Systems, ISSN 0950-7051, E-ISSN 1872-7409, Vol. 259, artikel-id 110081Artikel i tidskrift (Refereegranskat)
  • 7. Chughtai, F.
    et al.
    Ul Amin, R.
    Malik, A. S.
    Saeed, Nausheen
    Department of Computer Science, Sardar Bahadur Khan University, Pakistan.
    Performance analysis of microsoft network policy server and freeRADIUS authentication systems in 802.1x based secured wired ethernet using PEAP2019Ingår i: The International Arab Journal of Information Technology, ISSN 1683-3198, Vol. 16, nr 5, s. 862-870Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    IEEE 802.1x is an industry standard to implement physical port level security in wired and wireless Ethernets by using RADIUS infrastructure. Administrators of corporate networks need secure network admission control for their environment in a way that adds minimum traffic overhead and does not degrade the performance of the network. This research focuses on two widely used Remote Authentication Dial In User Service (RADIUS) servers, Microsoft Network Policy Server (NPS) and FreeRADIUS to evaluate their efficiency and network overhead according to a set of pre-defined key performance indicators using Protected Extensible Authentication Protocol (PEAP) in conjunction with Microsoft Challenged Handshake Authentication Protocol version 2 (MSCHAPv2). The key performance indicators – authentication time, reconnection time and protocol overhead were evaluated in real test bed configuration. Results of the experiments explain why the performance of a particular authentications system is better than the other in the given scenario. © 2019, Zarka Private University. All rights reserved.

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  • 8.
    Claesson, Christoffer
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Penetrationstest mot Webbapplikation2016Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Penetrationstest har blivit en populär metod för organisationer att se datasäkerheten ur en angripares ögon och ofta inkluderas både system och personal av ett penetrationstest.Rapporten går igenom attackerna SQL Injection, Cross-site Scripting (XSS) och Cross-site Request Forgery (CSRF) som är specifikt för webbapplikationer och även förslag på hur man kan motverka dessa. Syftet med studien är att kontrollera om företagets webbapplikation läcker information, eller om en användare kan komma förbi autentisering eller uppgradera sina rättigheter i webbapplikationen. Vidare beskrivs även vilka verktyg som används för att inhämta information för fortsatta analyser. Diskussionen kan användas som underlag för att skapa en säkrare webbapplikation. Rapporten avslutas med en indikering om att företaget har god säkerhet, fast även ytterligare underlag för framtida undersökningar. En exekutiv sammanställning av arbetet, se Bilaga 1.

  • 9.
    Dalberg, Daniel
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Client-side security for anonymous data collection from handsets2014Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    The widespread use of smartphones today, and the hardware available in these smartphones should make it possible to use these devices as a foundation for digital guides at, for example, tourist attractions with a historical connection. This report examines whether it is possible to create an Android application for smartphones that functions as a digital guide. Furthermore an attempt to delete sensitive data, in this case location data from the volatile memory on a smartphone running Android, is done. It turns out that a smartphone can be used as a foundation for a digital guide as long as the area that is to be covered by the guide is located in an area that is covered by the mobile networks and that it is possible to communicate with the GPS system. Deleting all the sensitive data from the volatile memory however, is more or less impossible.

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  • 10.
    Davari, Mahtab
    Högskolan Dalarna, Institutionen för information och teknik.
    Constructing and representing a knowledge graph(KG) for Positive Energy Districts (PEDs)2023Självständigt arbete på avancerad nivå (masterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    In recent years, knowledge graphs(KGs) have become essential tools for visualizing concepts and retrieving contextual information. However, constructing KGs for new and specialized domains like Positive Energy Districts (PEDs) presents unique challenges, particularly when dealing with unstructured texts and ambiguous concepts from academic articles. This study focuses on various strategies for constructing and inferring KGs, specifically incorporating entities related to PEDs, such as projects, technologies, organizations, and locations. We utilize visualization techniques and node embedding methods to explore the graph's structure and content and apply filtering techniques and t-SNE plots to extract subgraphs based on specific categories or keywords. One of the key contributions is using the longest path method, which allows us to uncover intricate relationships, interconnectedness between entities, critical paths, and hidden patterns within the graph, providing valuable insights into the most significant connections. Additionally, community detection techniques were employed to identify distinct communities within the graph, providing further understanding of the structural organization and clusters of interconnected nodes with shared themes. The paper also presents a detailed evaluation of a question-answering system based on the KG, where the Universal Sentence Encoder was used to convert text into dense vector representations and calculate cosine similarity to find similar sentences. We assess the system's performance through precision and recall analysis and conduct statistical comparisons of graph embeddings, with Node2Vec outperforming DeepWalk in capturing similarities and connections. For edge prediction, logistic regression, focusing on pairs of neighbours that lack a direct connection, was employed to effectively identify potential connections among nodes within the graph. Additionally, probabilistic edge predictions, threshold analysis, and the significance of individual nodes were discussed. Lastly, the advantages and limitations of using existing KGs(Wikidata and DBpedia) versus constructing new ones specifically for PEDs were investigated. It is evident that further research and data enrichment is necessary to address the scarcity of domain-specific information from existing sources.

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  • 11.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Exploring traffic systems by elasticity analysis of neural networks2019Ingår i: Neural Networks in Transport Applications, Taylor and Francis , 2019, s. 211-228Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    This chapter shows how elasticity testing of neural networks can greatly aid our understanding of transport systems. It examines several different pieces of work which have the common theme of using neural networks, coupled with a technique of elasticity analysis, in order to reach a better understanding of transport related problems. One of the main reasons for using neural networks is that they can easily represent complex functions, often with nonlinear interactions between different parameters. The chapter focuses on the elasticity of a single parameter with respect to a single network output. However, the elasticity technique can easily be extended to explore mutual interactions between parameters. A three-dimensional elasticity plot is shown of elasticity response against occupancy and speed. When neural networks are coupled with advanced computer visualization tools they provide an immensely powerful tool for general analysis. © V. Himanen, P. Nijkamp, A. Reggiani and J. Raitio 1998. All rights reserved.

  • 12.
    Du, Xiaofeng
    et al.
    British Telecommun PLC, London, England..
    Song, William Wei
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Quality improvement framework for business oriented geo-spatial data2015Ingår i: Web Information Systems Engineering - WISE 2014 workshops, Berlin: Springer Berlin/Heidelberg, 2015, Vol. 9051, s. 239-249Konferensbidrag (Refereegranskat)
    Abstract [en]

    In the past few years, Geo-spatial data quality has received increasing attention and concerns. As more and more business decisions are made based on data analytic result from geo-spatial related data, low quality data means wrong or inappropriate decisions, which could have substantial effects on a business's future. In this paper, we propose a framework that can systematically ensure and improve geo-spatial data quality throughout the whole life cycle of data.

  • 13.
    Fridolfsson, Thomas
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Forensisk undersökning av Amazon Kindle2015Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Detta arbete har genomförts i samarbete med Försvarsmakten och behandlar vilka möjligheter som finns för forensiska undersökningar av e-boksläsaren Amazon Kindle. I arbetets litteraturstudie beskrivs hur tidigare forskning inom ämnet är kraftigt begränsad. Arbetet syftar därför till att besvara hur data kan extraheras från en Kindle, vilka data av forensiskt intresse en Kindle kan innehålla, var denna information lagras och om detta skiljer sig åt mellan olika modeller och firmware-versioner samt om det är nog att undersöka endast den del av minnet som är tillgänglig för användaren eller om ytterligare privilegier för att komma åt hela minnesarean bör införskaffas.

    För att göra detta fylls tre olika modeller av Kindles med information. Därefter tas avbilder på dem, dels på endast användarpartitionen och dels på dess fullständiga minnesarea efter att en privilegie-eskalering har utförts. Inhämtad data analyseras och resultatet presenteras.

    Resultatet visar att information av forensiskt intresse så som anteckningar, besökta webbsidor och dokument kan återfinnas, varför det finns ett värde i att utföra forensiska undersökningar på Amazon Kindles. Skillnader råder mellan vilken information som kan återfinnas och var den lagras på de olika enheterna. Enheterna har fyra partitioner varav endast en kan kommas åt utan privilegie-eskalering, varför det finns en fördel med att inhämta avbilder av hela minnesarean.

    Utöver ovanstående presenteras en metod för att förbipassera en enhets kodlås och därigenom få fullständig åtkomst till den även om den är låst.

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  • 14. Goldsby, H. J.
    et al.
    Young, R. L.
    Schossau, J.
    Hofmann, H. A.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Serendipitous scaffolding to improve a genetic algorithm's speed and quality2018Ingår i: GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference, Association for Computing Machinery, Inc , 2018, s. 959-966Konferensbidrag (Refereegranskat)
    Abstract [en]

    A central challenge to evolutionary computation is enabling techniques to evolve increasingly complex target end products. Frequently, direct approaches that reward only the target end product itself are not successful because the path between the starting conditions and the target end product traverses through a complex fitness landscape, where the directly accessible intermediary states may be require deleterious or even simply neutral mutations. As such, a host of techniques have sprung up to support evolutionary computation techniques taking these paths. One technique is scaffolding where intermediary targets are used to provide a path from the starting state to the end state. While scaffolding can be successful within well-understood domains it also poses the challenge of identifying useful intermediaries. Within this paper we first identify some shortcomings of scaffolding approaches ' namely, that poorly selected intermediaries may in fact hurt the evolutionary computation's chance of producing the desired target end product. We then describe a light-weight approach to selecting intermediate scaffolding states that improve the efficacy of the evolutionary computation. © 2018 Association for Computing Machinery.

  • 15.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Kulturgeografi.
    Rebreyend, Pascal
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    How does the use of different road networks effect the optimal location of facilities in rural areas?2012Rapport (Övrigt vetenskapligt)
    Abstract [en]

    The p-median problem is often used to locate P service facilities in a geographically distributed population. Important for the performance of such a model is the distance measure.

    Distance measure can vary if the accuracy of the road network varies. The rst aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the road network is alternated. It is hard to nd an exact optimal solution for p-median problems. Therefore, in this study two heuristic solutions are applied, simulating annealing and a classic heuristic. The secondary aim is to compare the optimal location solutions using dierent algorithms for large p-median problem. The investigation is conducted by the means of a case study in a rural region with an asymmetrically distributed population, Dalecarlia.

    The study shows that the use of more accurate road networks gives better solutions for optimal location, regardless what algorithm that is used and regardless how many service facilities that is optimized for. It is also shown that the simulated annealing algorithm not just is much faster than the classic heuristic used here, but also in most cases gives better location solutions.

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  • 16.
    Hedly, Josefin
    et al.
    Högskolan Dalarna, Institutionen för information och teknik.
    De Young, Mikaela
    Högskolan Dalarna, Institutionen för information och teknik.
    Uncertainty Analysis: Severe Accident Scenario at a Nordic Nuclear Power Plant2023Självständigt arbete på avancerad nivå (masterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Nuclear Power Plants (NPP) undergo fault and sensitivity analysis with scenario modelling to predict catastrophic events, specifically releases of Cesium 137 (Cs-137). The purpose of this thesis is to find which of 108 input-features from Modular Accident Analysis Program (MAAP)simulation code are important, when there is large release of Cs-137 emissions. The features are tested all together and in their groupings. To find important features, the Machine learning (ML) model Random Forest (RF) has a built-in attribute which identifies important features. The results of RF model classification are corroborated with Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and use k-folds cross validation to improve and validate the results, resulting in a near 90% accuracy for the three ML models. RF is successful at identifying important features related to Cs-137 emissions, by using the classification model to first identify top features, to further train the models at identifying important input-features. The discovered input-features are important both within their individual groups, but also when including all features simultaneously. The large number of features included did not disrupt RF much, but the skewed dataset with few classified extreme events caused the accuracy to be lower at near 90%.

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  • 17.
    Hellsten, Maria
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Utvärdering av forensiska verktyg och teknikerför handhållna enheter2015Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    In recent years, the market for mobile devices has increased and technological developments haveadvanced in a rapid pace. More and more communication is done through mobile and handhelddevices. Therefore, even the seizures increase by these devices, because there is often very valuableforensic information stored on them. The purpose of this work is based on a method combining aqualitative and a quantitative study examining forensic tools and techniques for handheld devices.The paper analyzed forensics description of chip-off technology and its implementation, as well asCellebrites different extraction methods are tested to see what information that is possible to extractwith each method.To seek answers, literature studies, own investigations and semi-structured interviews have beenconducted. The report is divided into different sections, where the current operating systemsAndroid and iOS, Cellebrites tools and chip-off technique are described, own investigations made,the interviews carried out and the results thereof. The results show that the type of information thatis possible to obtain, differs depending on the extraction method. Chip-off technology allows a fullphysical extraction on handheld devices that can be locked and broken, but requires some training,experience and specialized equipment.

  • 18. Hintze, Arend
    Open-endedness for the sake of open-endedness2019Ingår i: Artificial Life, ISSN 1064-5462, E-ISSN 1530-9185, Vol. 25, nr 2, s. 198-206Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Natural evolution keeps inventing new complex and intricate forms and behaviors. Digital evolution and genetic algorithms fail to create the same kind of complexity, not just because we still lack the computational resources to rival nature, but because (it has been argued) we have not understood in principle how to create open-ended evolving systems. Much effort has been made to define such open-endedness so as to create forms of increasing complexity indefinitely. Here, however, a simple evolving computational system that satisfies all such requirements is presented. Doing so reveals a shortcoming in the definitions for open-ended evolution. The goal to create models that rival biological complexity remains. This work suggests that our current definitions allow for even simple models to pass as open-ended, and that our definitions of complexity and diversity are more important for the quest of open-ended evolution than the fact that something runs indefinitely. © 2019 Massachusetts Institute of Technology.

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  • 19.
    Hintze, Arend
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Michigan State University, East Lansing, MI, USA.
    Adami, Christoph
    Michigan State University, East Lansing, MI, USA; .
    Detecting Information Relays in Deep Neural Networks2023Ingår i: Entropy, E-ISSN 1099-4300, Vol. 25, nr 3, artikel-id 401Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Deep learning of artificial neural networks (ANNs) is creating highly functional processes that are, unfortunately, nearly as hard to interpret as their biological counterparts. Identification of functional modules in natural brains plays an important role in cognitive and neuroscience alike, and can be carried out using a wide range of technologies such as fMRI, EEG/ERP, MEG, or calcium imaging. However, we do not have such robust methods at our disposal when it comes to understanding functional modules in artificial neural networks. Ideally, understanding which parts of an artificial neural network perform what function might help us to address a number of vexing problems in ANN research, such as catastrophic forgetting and overfitting. Furthermore, revealing a network's modularity could improve our trust in them by making these black boxes more transparent. Here, we introduce a new information-theoretic concept that proves useful in understanding and analyzing a network's functional modularity: the relay information IR. The relay information measures how much information groups of neurons that participate in a particular function (modules) relay from inputs to outputs. Combined with a greedy search algorithm, relay information can be used to identify computational modules in neural networks. We also show that the functionality of modules correlates with the amount of relay information they carry.

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  • 20.
    Hintze, Arend
    et al.
    Michigan State University, East Lansing, United States.
    Kirkpatrick, D.
    Adami, C.
    The structure of evolved representations across different substrates for artificial intelligence2020Ingår i: ALIFE 2018 - 2018 Conference on Artificial Life: Beyond AI, MIT Press , 2020, s. 388-395Konferensbidrag (Refereegranskat)
    Abstract [en]

    Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable to misdirection. Small amounts of noise can significantly affect their ability to correctly complete a task. Instead of generalizing concepts, ANNs seem to focus on surface statistical regularities in a given task. Here we compare how recurrent artificial neural networks, long short-term memory units, and Markov Brains sense and remember their environments. We show that information in Markov Brains is localized and sparsely distributed, while the other neural network substrates “smear” information about the environment across all nodes, which makes them vulnerable to noise. Copyright © ALIFE 2018.All rights reserved.

  • 21.
    Hintze, Arend
    et al.
    Michigan State University, East Lansing, United States.
    Olson, R. S.
    Lehman, J.
    Orthogonally evolved AI to improve difficulty adjustment in video games2016Ingår i: Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science, vol 9597 / [ed] Squillero G., Burelli P., Springer Verlag , 2016, Vol. 9597, s. 525-540Konferensbidrag (Refereegranskat)
    Abstract [en]

    Computer games are most engaging when their difficulty is well matched to the player’s ability, thereby providing an experience in which the player is neither overwhelmed nor bored. In games where the player interacts with computer-controlled opponents, the difficulty of the game can be adjusted not only by changing the distribution of opponents or game resources, but also through modifying the skill of the opponents. Applying evolutionary algorithms to evolve the artificial intelligence that controls opponent agents is one established method for adjusting opponent difficulty. Less-evolved agents (i.e., agents subject to fewer generations of evolution) make for easier opponents, while highlyevolved agents are more challenging to overcome. In this publication we test a new approach for difficulty adjustment in games: orthogonally evolved AI, where the player receives support from collaborating agents that are co-evolved with opponent agents (where collaborators and opponents have orthogonal incentives). The advantage is that game difficulty can be adjusted more granularly by manipulating two independent axes: by having more or less adept collaborators, and by having more or less adept opponents. Furthermore, human interaction can modulate (and be informed by) the performance and behavior of collaborating agents. In this way, orthogonally evolved AI both facilitates smoother difficulty adjustment and enables new game experiences. © Springer International Publishing Switzerland 2016.

  • 22.
    Hägg, Andreas
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Säker hantering av mobila enheter i organisationers IT-miljö2014Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Marknaden för mobila enheter och mobilitet har haft en kraftig utveckling de

    senaste åren och den tekniska utvecklingen har gått snabbt fram. Grunden till en

    ökad mobilitet inom förtaget ligger i att den ofta förväntas ge positiva effekter i

    ökad affärsnytta och produktivitet hos de anställda. Det är ändå viktigt att komma

    ihåg att det även finns risker med en ökad mobilitet. Risker som organisationer bör

    ta hänsyn till. Tester gjorda i detta arbete visar att det finns mycket information

    lagrad på mobila enheter. Delar av denna information kan även vara mycket

    känslig information.

    Smartphones och andra mobila enheters karakteristiska egenskaper, främst att de

    är små och bärbara, gör att de är extra sårbara för ett flertal risker och hot. De

    största hotet mot säkerheten i samband med mobila enheter anses vara risken för

    dataförlust. Denna förlust sker ofta i samband med att mobila enheter avvecklas,

    stjäls eller tappas bort. Ett annat stort hot är att information stjäls av eller sprids på

    grund av att skadlig programkod (malware) finns på enheten.

    För att komma till rätta med dessa risker och hot så behöver man titta på hur

    organisationen ska hantera situationen. Det finns på marknaden säkerhetslösningar

    för att hantera många delar av säkerhetsproblematiken. Mobile Device

    Management (MDM) är en funktionalitet som handlar om att skapa kontroll på

    tillgångar och information samt möjliggöra en säkrare användning av mobila

    enheter. Ett benchmarktest och jämförelseanalys av ett urval av marknadsledande

    MDM verktyg visar på att det finns ett gott stöd för flera säkerhetshöjande

    funktioner.

    AirWatch, MobileIron och XenMobile är tre olika MDM verktyg som alla ger ett

    bra stöd för standardiserade säkerhetshöjande funktioner för mobila enheter. Att

    göra ett bra val av verktyg kräver dock ett förarbete. Detta är nödvändigt för att

    skapa rätta förutsättningar för ett val som stödjer organisationens behov, krav och

    strategier.

  • 23.
    Höglund, Rikard
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Anonymitet på Internet: Tor och steganografi i nätverkstrafik2014Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
  • 24.
    Ilar, Pontus
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Server-Side Security for Anonymous Data Collection from Handset2014Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Digital guides are something that starts to appear more and more these days. Several digital

    guides have already been tried with mixed results. One version was developed for

    “världsarvet Falun” where a PDA was inserted into a box with speakers and a GPS and short

    videos gives information about certain locations in Falun. Another guide was developed for

    the IPhone in which static maps were displayed with information rendered by a server. This

    thesis tries to develop a server side solution for an application designed to give users a tour of

    selected so called areas of interest (aoi). The server only provides coordinates and information

    about aois and the specific points within. The solution uses a server with a database to store

    and provide information about these areas. The first prototype for the solution uses https to

    communicate with the application and SQL PDO queries to communicate with the database.

    Future work with this solution could be to upgrade the server to facilitate a larger scalability

    for the solution.

  • 25. Jahns, J.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    How the integration of group and individual level selection affects the evolution of cooperation2020Ingår i: ALIFE 2018 - 2018 Conference on Artificial Life: Beyond AI, MIT Press , 2020, s. 530-535Konferensbidrag (Refereegranskat)
    Abstract [en]

    Many evolutionary models that explore the emergence of cooperation rely on either individual level selection or group level selection. However, natural systems are often more complex and selection is never just on the level of the individual or group alone. Here we explore how systems of collaborating agents evolve when selection is based on a mixture of group and individual performances. It has been suggested that under such situations free riders thrive and hamper evolution significantly. Here we show that free rider effects can almost be ignored. Sharing resources even with free riders benefits the evolution of cooperators, which in the long run is more beneficial than the short term cost. Copyright © ALIFE 2018.All rights reserved.

  • 26.
    Jalali, Ali
    et al.
    Högskolan Dalarna, Institutionen för information och teknik.
    Assadi, Laila
    Högskolan Dalarna, Institutionen för information och teknik.
    Osman, Asma
    Högskolan Dalarna, Institutionen för information och teknik.
    Password Management: A Study about Current Challenges with Password Management2023Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Effective password management is crucial for safeguarding online accounts and sensitive information. This research examines the current challenges and provides alternative solutions for better password management. This study encompasses a comprehensive survey and interviews conducted with individuals across various professional backgrounds. A total of 137 online users participated in the survey, which spanned over a duration of 15 days. Additionally, four individuals were interviewed to gather more indepth data. The study aimed to understand password selection behaviors and the factors influencing them. The goal is to develop practical strategies to enhance password security and mitigate unauthorized access to sensitive information.

    The purpose of the study is to provide valuable insights into the complexities of password management and contribute to the development of informed approaches for stronger password security. The study emphasizes the significance of password management and highlights the importance of educating users about the risks associated with weak passwords. The findings have implications not only for the research community but also for individuals and organizations seeking to understand user behavior and attitudes towards password systems. By gaining a deeper understanding of these aspects, it becomes possible to design more effective strategies to protect online accounts and sensitive data. 

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  • 27. Kirkpatrick, D.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Augmenting neuro-evolutionary adaptation with representations does not incur a speed accuracy trade-off2019Ingår i: GECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion, Association for Computing Machinery, Inc , 2019, s. 177-178Konferensbidrag (Refereegranskat)
    Abstract [en]

    Representations, or sensor-independent internal models of the environment, are important for any type of intelligent agent to process and act in an environment. Imbuing an artificially intelligent system with such a model of the world it functions in remains a difficult problem. However, using neuro-evolution as the means to optimize such a system allows the artificial intelligence to evolve proper models of the environment. Previous work has found an information-theoretic measure, R, which measures how much information a neural computational architecture (henceforth loosely referred to as a brain) has about its environment, and can additionally be used speed up the neuro-evolutionary process. However, it is possible that this improved evolutionary adaptation comes at a cost to the brain's ability to generalize or the brain's robustness to noise. In this paper, we show that this is not the case; to the contrary, we find an improved ability of the to evolve in noisy environments when the neuro-correlate R is used to augment evolutionary adaptation. © 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.

  • 28. Kirkpatrick, D.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    The role of ambient noise in the evolution of robust mental representations in cognitive systems2020Ingår i: Proceedings of the 2019 Conference on Artificial Life: How Can Artificial Life Help Solve Societal Challenges, ALIFE 2019, MIT Press , 2020, s. 432-439Konferensbidrag (Refereegranskat)
    Abstract [en]

    Natural environments are full of ambient noise; nevertheless, natural cognitive systems deal greatly with uncertainty but also have ways to suppress or ignore noise unrelated to the task at hand. For most intelligent tasks, experiences and observations have to be committed to memory and these representations of reality inform future decisions. We know that deep learned artificial neural networks (ANNs) often struggle with the formation of representations. This struggle may be due to the ANN's fully interconnected, layered architecture. This forces information to be propagated over the entire system, which is different from natural brains that instead have sparsely distributed representations. Here we show how ambient noise causes neural substrates such as recurrent ANNs and long short-term memory neural networks to evolve more representations in order to function in these noisy environments, which also greatly improves their functionality. However, these systems also tend to further smear their representations over their internal states making them more vulnerable to internal noise. We also show that Markov Brains (MBs) are mostly unaffected by ambient noise, and their representations remain sparsely distributed (i.e. not smeared). This suggests that ambient noise helps to increase the amount of representations formed in neural networks, but also requires us to find additional solutions to prevent smearing of said representations. Copyright © ALIFE 2019.All rights reserved.

  • 29. Kvam, P. D.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Rewards, risks, and reaching the right strategy: Evolutionary paths from heuristics to optimal decisions2018Ingår i: Evolutionary Behavioral Sciences, ISSN 2330-2925, E-ISSN 2330-2933, Vol. 12, nr 3, s. 177-190Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Theories of decision-making often posit optimal or heuristic strategies for performing a task. In optimal strategies, information is integrated over time in order to achieve the ideal outcomes; in the heuristic case, some shortcut or simplification is applied in order to make the decision faster or easier. In this article, we use a computational framework to study the evolution of both types of decision strategies in artificial agents. The fitness of these agents is assessed based on their performance on a sequential decision task where they must accurately identify the source of as many incoming information signals as they can over a finite time span. In order to examine what decision strategies evolve as a function of task characteristics, we manipulate the quality of decision information (difficulty) and the magnitude of punishments for incorrect answers. We find that trivial (but optimal) strategies evolve when punishment magnitude is lower than the reward magnitude for correct answers, and optimal information-integrating strategies evolve when either punishment magnitude is low or information quality is high. However, the computational demands of the task become much greater as information quality decreases and punishment magnitudes increase. In these cases, heuristics are used to maintain decision accuracy in spite of the limited cognitive resources agents have available. The results suggest that heuristics are an evolved response to environments with high demands on cognitive resources, where optimal strategies are particularly difficult to achieve. © 2018 American Psychological Association.

  • 30.
    Li, Yujiao
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Fleyeh, Hasan
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Twitter Sentiment Analysis of New IKEA Stores Using Machine Learning2018Ingår i: 2018 International Conference on Computer and Applications, ICCA 2018, 2018, s. 4-11, artikel-id 8460277Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper studied public emotion and opinion concerning the opening of new IKEA stores, specifically, how much attention are attracted, how much positive and negative emotion are aroused, what IKEA-related topics are talked due to this event. Emotion is difficult to measure in retail due to data availability and limited quantitative tools. Twitter texts, written by the public to express their opinion concerning this event, are used as a suitable data source to implement sentiment analysis. Around IKEA opening days, local people post IKEA related tweets to express their emotion and opinions on that. Such “IKEA” contained tweets are collected for opinion mining in this work. To compute sentiment polarity of tweets, lexiconbased approach is used for English tweets, and machine learning methods for Swedish tweets. The conclusion is new IKEA store are paid much attention indicated by significant increasing tweets frequency, most of them are positive emotions, and four studied cities have different topics and interests related IKEA. This paper extends knowledge of consumption emotion studies of prepurchase, provide empirical analysis of IKEA entry effect on emotion. Moreover, it develops a Swedish sentiment prediction model, elastic net method, to compute Swedish tweets’ sentiment polarity which has been rarely conducted.  

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    TwitterIkea
  • 31.
    Lindstrand, Martin
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Laws, Logs and Forensic Traceability: Case Study: Windows server applications (firewall, webserver, exchange server)2016Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    The thesis has investigated the problem of information logging and storage from a forensic standpoint; and the study has been carried out towards a company which cannot be named for confidential reasons. what information should be logged and what has to be logged according to the Swedish law, and for how long time should the logs be saved. The problem here is to find out what in general should be saved into a log file so it can be applied to as many systems as possible. The thesis has also investigated when to do a forensic investigation and what to log in Microsoft server environment (webserver, firewall and exchange server).

    I intend to solve the problem by describing what needs to be saved in general and find out if there is a law that demands information to be logged. I intend to find out what needs to be saved from a forensic point of view. I am going to find out what should be logged on Microsoft server applications: webserver, firewall and exchange server.

    The method used to solve the problem was with practical study and literature study. Practical study where made on a Microsoft server 2012 on webserver, firewall and exchange server where I looked at the log files and what information that were saved to them.

    The report finds out what should be saved in a log file from a forensic point of view and what needs to be saved according to Swedish law. The report finds how long time the log files have to be saved. The report finds when to do a forensic investigation and what to investigate.

  • 32.
    Malek, Wasim
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Big Data Analysis in Social Networks: Extracting Food Preferences of Vegans from Twitter2016Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Market research is often conducted through conventional methods such as surveys, focus

    groups and interviews. But the drawbacks of these methods are that they can be costly and timeconsuming.

    This study develops a new method, based on a combination of standard techniques

    like sentiment analysis and normalisation, to conduct market research in a manner that is free

    and quick. The method can be used in many application-areas, but this study focuses mainly on

    the veganism market to identify vegan food preferences in the form of a profile.

    Several food words are identified, along with their distribution between positive and negative

    sentiments in the profile. Surprisingly, non-vegan foods such as cheese, cake, milk, pizza and

    chicken dominate the profile, indicating that there is a significant market for vegan-suitable

    alternatives for such foods. Meanwhile, vegan-suitable foods such as coconut, potato,

    blueberries, kale and tofu also make strong appearances in the profile.

    Validation is performed by using the method on Volkswagen vehicle data to identify positive

    and negative sentiment across five car models. Some results were found to be consistent with

    sales figures and expert reviews, while others were inconsistent. The reliability of the method

    is therefore questionable, so the results should be used with caution.

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  • 33. Marstaller, L.
    et al.
    Hintze, Arend
    Michigan State University, East Lansing, United States.
    Adami, C.
    The evolution of representation in simple cognitive networks2013Ingår i: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 25, nr 8, s. 2079-2107Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Representations are internalmodels of the environment that can provide guidance to a behaving agent, even in the absence of sensory information. It is not clear how representations are developed and whether they are necessary or even essential for intelligent behavior.We argue here that the ability to represent relevant features of the environment is the expected consequence of an adaptive process, give a formal definition of representation based on information theory, and quantify it with a measure R. To measure how R changes over time, we evolve two types of networks-an artificial neural network and a network of hiddenMarkov gates-to solve a categorization task using a genetic algorithm. We find that the capacity to represent increases during evolutionary adaptation and that agents form representations of their environment during their lifetime. This ability allows the agents to act on sensorial inputs in the context of their acquired representations and enables complex and context-dependent behavior. We examine which concepts (features of the environment) our networks are representing, how the representations are logically encoded in the networks, and how they form as an agent behaves to solve a task. We conclude that R should be able to quantify the representations within any cognitive system and should be predictive of an agent's long-term adaptive success. © 2013 Massachusetts Institute of Technology.

  • 34.
    Mehra, Priyanka
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Varshney, A. K.
    HFedRF: Horizontal Federated Random Forest2024Ingår i: International Congress and Workshop on Industrial AI and eMaintenance 2023. IAI 2023. Lecture Notes in Mechanical Engineering. / [ed] Kumar, U., Karim, R., Galar, D., Kour, R, Springer Science and Business Media Deutschland GmbH , 2024, s. 409-422Konferensbidrag (Refereegranskat)
    Abstract [en]

    Real-world data is typically dispersed among numerous businesses or governmental agencies, making it difficult to integrate them into data privacy laws like the General Data Protection Regulation of the European Union (GDPR). Two significant obstacles to the use of machine learning models in applications are the existence of such data islands and privacy issues. In this paper, we address these issues and propose ‘HFedRF: Horizontal Federated Random Forest’, a privacy-preserving federated model which is approximately lossless. Our proposed algorithm merges d random forests computed on d different devices and returns a global random forest which is used for prediction on local devices. In our methodology, we compare IIDs (Independent and Identically Distributed) and non-IIDs variant of our algorithm HFedRF with traditional machine learning (ML) methods i.e., decision tree and random forest. Our results show that we achieve benchmark comparable results with our algorithm for IID as well as non-IID settings of federated learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

  • 35.
    Memedi, Mevludin
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Sadikov, Aleksander
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Groznik, Vida
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Žabkar, Jure
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Možina, Martin
    Faculty of Information Science, Artificial Intelligence Laboratory, University of Ljubljana, Ljubljana, Slovenia.
    Bergquist, Filip
    Sahlgrenska Academy, Department of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Johansson, Anders
    Neurology, Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
    Haubenberger, Dietrich
    NINDS Intramural Research Program, Clinical Trials Unit, National Institutes of Health, Bethesda, MD, USA.
    Nyholm, Dag
    Neurology, Neuroscience, Uppsala University, Uppsala, Sweden.
    Automatic spiral analysis for objective assessment of motor symptoms in Parkinson's disease2015Ingår i: Sensors, E-ISSN 1424-8220, Vol. 15, nr 9, s. 23727-23744Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

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    fulltext
  • 36.
    Memedi, Mevludin
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik. Örebro universitet.
    Thomas, Ilias
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Nyholm, Dag
    Uppsala University Hospital.
    Westin, Jerker
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Senek, Marina
    Uppsala University Hospital.
    Medvedev, Alexander
    Uppsala University.
    Askmark, Håkan
    Uppsala University.
    Aquilonius, Sten-Magnus
    Uppsala University.
    Bergquist, Filip
    University of Gothenburg.
    Constantinescu, Radu
    Ohlsson, Fredrik
    Acreo AB.
    Spira, Jack
    Sensidose AB.
    Lycke, Sara
    Cenvigo AB.
    Ericsson, Anders
    Acreo AB.
    Construction of a levodopa-response index from wearable sensorsfor quantifying Parkinson’s disease motor functions: Preliminary results2016Konferensbidrag (Övrigt vetenskapligt)
    Ladda ner fulltext (pdf)
    fulltext
  • 37.
    Meng, Xiangli
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Carling, Kenneth
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    How to decide upon stopping a heuristic algorithm in facility-location problems?2014Ingår i: Web Information Systems Engineering – WISE 2013 Workshops: WISE 2013 International Workshops BigWebData, MBC, PCS, STeH, QUAT, SCEH, and STSC 2013, Nanjing, China, October 13-15, 2013, Revised Selected Papers / [ed] Zhisheng Huang, Chengfei Liu, Jing He, Guangyan Huang, Berlin: Springer Berlin/Heidelberg, 2014, Vol. 8182, s. 280-283Konferensbidrag (Refereegranskat)
    Abstract [en]

    Solutions to combinatorial optimization, such as p-median problems of locating facilities, frequently rely on heuristics to minimize the objective function. The minimum is sought iteratively and a criterion is needed to decide when the procedure (almost) attains it. However, pre-setting the number of iterations dominates in OR applications, which implies that the quality of the solution cannot be ascertained. In this paper we compare the methods proposed previous literate of estimating minimum, and propose some thought of it.

  • 38.
    Millet, Patric
    et al.
    Mittuniversitetet, Institutionen för samhällsvetenskap.
    Sandberg, Karl W
    Högskolan Dalarna, Akademin Industri och samhälle, Arbetsvetenskap. Mittuniversitetet, Institutionen för informationsteknologi och medier.
    Impact of locus of control how owner-manager's perceive network usage and value in a small industrial park in rural Sweden2005Ingår i: Uddevalla symposium 2005 innovations and entrepreneurship in functional regions: papers presented at the 8 Uddevalla Symposium and the 8 McGill International Entrepreneurship Conference,15-17 September, Uddevalla, Sweden, Trollhättan: University West , 2005, s. 559-575Konferensbidrag (Refereegranskat)
  • 39.
    Ngoc Phuong, Chau
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Machine Learning Approaches to Develop Weather Normalize Models for Urban Air Quality2024Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    According to the World Health Organization, almost all human population (99%) lives in 117 countries with over 6000 cities, where air pollutant concentration exceeds recommended thresholds. The most common, so-called criteria, air pollutants that affect human lives, are particulate matter (PM) and gas-phase (SO2, CO, NO2, O3 and others). Therefore, many countries or regions worldwide have imposed regulations or interventions to reduce these effects. Whenever an intervention occurs, air quality changes due to changes in ambient factors, such as weather characteristics and human activities. One approach for assessing the effects of interventions or events on air quality is through the use of the Weather Normalized Model (WNM). However, current deterministic models struggle to accurately capture the complex, non-linear relationship between pollutant concentrations and their emission sources. Hence, the primary objective of this thesis is to examine the power of machine learning (ML) and deep learning (DL) techniques to develop and improve WNMs. Subsequently, these enhanced WNMs are employed to assess the impact of events on air quality. Furthermore, these ML/DL-based WNMs can serve as valuable tools for conducting exploratory data analysis (EDA) to uncover the correlations between independent variables (meteorological and temporal features) and air pollutant concentrations within the models. 

    It has been discovered that DL techniques demonstrated their efficiency and high performance in different fields, such as natural language processing, image processing, biology, and environment. Therefore, several appropriate DL architectures (Long Short-Term Memory - LSTM, Recurrent Neural Network - RNN, Bidirectional Recurrent Neural Network - BIRNN, Convolutional Neural Network - CNN, and Gated Recurrent Unit - GRU) were tested to develop the WNMs presented in Paper I. When comparing these DL architectures and Gradient Boosting Machine (GBM), LSTM-based methods (LSTM, BiRNN) have obtained superior results in developing WNMs. The study also showed that our WNMs (DL-based) could capture the correlations between input variables (meteorological and temporal variables) and five criteria contaminants (SO2, CO, NO2, O3 and PM2.5). This is because the SHapley Additive exPlanations (SHAP) library allowed us to discover the significant factors in DL-based WNMs. Additionally, these WNMs were used to assess the air quality changes during COVID-19 lockdown periods in Ecuador. The existing normalized models operate based on the original units of pollutants and are designed for assessing pollutant concentrations under “average” or consistent weather conditions. Predicting pollution peaks presents an even greater challenge because they often lack discernible patterns. To address this, we enhanced the Weather Normalized Models (WNMs) to boost their performance specifically during daily concentration peak conditions. In the second paper, we accomplished this by developing supervised learning techniques, including Ensemble Deep Learning methods, to distinguish between daily peak and non-peak pollutant concentrations. This approach offers flexibility in categorizing pollutant concentrations as either daily concentration peaks or non-daily concentration peaks. However, it is worth noting that this method may introduce potential bias when selecting non-peak values. In the third paper, WNMs are directly applied to daily concentration peaks to predict and analyse the correlations between meteorological, temporal features and daily concentration peaks of air pollutants.

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  • 40.
    Ngoc Phuong, Chau
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Zalakeviciute, Rasa
    Grupo de Biodiversidad Medio Ambiente y Salud, Universidad de Las Américas, Quito, Ecuador.
    Thomas, Ilias
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Rybarczyk, Yves
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito2022Ingår i: Frontiers in Big Data, ISSN 2624-909X, Vol. 5, artikel-id 842455Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO<sub>2</sub>, PM<sub>2.5</sub>, SO<sub>2</sub>, and O<sub>3</sub>) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: −48.75%, for CO, −45.76%, for SO<sub>2</sub>, −42.17%, for PM<sub>2.5</sub>, and −63.98%, for NO<sub>2</sub>. The reduction of this latter gas has induced an increase of O<sub>3</sub> by +26.54%.

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  • 41.
    Nyberg, Mattias
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Changing to a Biometric Access System: and Its Effect on the Work Environment2018Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
  • 42.
    Nyberg, Roger G.
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik. School of Engineering and the Built Environment, Edinburgh Napier University, EH10 5DT Edinburgh, UK.
    Gupta, Narendra K.
    School of Engineering and the Built Environment, Edinburgh Napier University, EH10 5DT Edinburgh, UK.
    Yella, Siril
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Dougherty, Mark
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Monitoring vegetation on railway embankments: supporting maintenance decisions2013Ingår i: Proceedings of the 2013 International Conference on Ecology and Transportation, 2013, s. 1-18Konferensbidrag (Refereegranskat)
    Abstract [en]

    The national railway administrations in Scandinavia, Germany, and Austria mainly resort to manual inspections to control vegetation growth along railway embankments. Manually inspecting railways is slow and time consuming. A more worrying aspect concerns the fact that human observers are often unable to estimate the true cover of vegetation on railway embankments. Further human observers often tend to disagree with each other when more than one observer is engaged for inspection. Lack of proper techniques to identify the true cover of vegetation even result in the excess usage of herbicides; seriously harming the environment and threating the ecology. Hence work in this study has investigated aspects relevant to human variationand agreement to be able to report better inspection routines. This was studied by mainly carrying out two separate yet relevant investigations.First, thirteen observers were separately asked to estimate the vegetation cover in nine imagesacquired (in nadir view) over the railway tracks. All such estimates were compared relatively and an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05). Bearing in difference between the observers, a second follow-up field-study on the railway tracks was initiated and properly investigated. Two railway segments (strata) representingdifferent levels of vegetationwere carefully selected. Five sample plots (each covering an area of one-by-one meter) were randomizedfrom each stratumalong the rails from the aforementioned segments and ten images were acquired in nadir view. Further three observers (with knowledge in the railway maintenance domain) were separately asked to estimate the plant cover by visually examining theplots. Again an analysis of variance resulted in a significant difference on the observers’ cover estimates (p<0.05) confirming the result from the first investigation.The differences in observations are compared against a computer vision algorithm which detects the "true" cover of vegetation in a given image. The true cover is defined as the amount of greenish pixels in each image as detected by the computer vision algorithm. Results achieved through comparison strongly indicate that inconsistency is prevalent among the estimates reported by the observers. Hence, an automated approach reporting the use of computer vision is suggested, thus transferring the manual inspections into objective monitored inspections

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  • 43.
    Olsson, Jonas
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Android file browsing: Comparing file explorer features andforensic artefacts2015Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [sv]

    Målet med detta arbete var att avgöra vilken sorts spår som lämnas efter användning av de mest populärafilhanterarna för Android. Detta undersöktes genom att gå igenom deras funktioner, bestämma passande dataoch handlingar att utföra med apparna och sedan gå igenom dem en och en. Mellan de olika stegen sparadesfilerna skapade av apparna som testades för att se vad som ändrats i vilket steg.Undersökningen av dessa filer visade att det är många olika sorters spår som lämnas beroende på appensfunktioner. Vissa fokuserar på information om enheten i sig medan andra är mer inställda på information omfilerna på enheten. Vissa katalogiserar till och med alla filer, deras storlekar och var på enheten de fanns.Många av apparna sparade även ikoner av bilder som användaren hade tittat på, och behöll dessa ikoner ävenom bilden själv har tagits bort.All information använd av apparna kunde inte samlas in eftersom platsen där den lagrades inte hann påträffasinnan arbetets slut. Informationen som gick att få ut är dock tillräcklig för att kunna konstatera att det finnspotentiellt värde i att inkludera denna typ av appar när man utför en forensisk undersökning.

  • 44.
    Paidi, Vijay
    Högskolan Dalarna, Institutionen för information och teknik.
    Short-term prediction of parking availability in an open parking lotManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    The parking of cars is a globally recognized problem, especially at locations where there is a high demand for empty parking spaces. Drivers tend to cruise additional distances while searching for empty parking spaces during peak hours leading to problems such as pollution, congestion, and driver frustration. Providing short-term predictions of parking availability would facilitate the driver in making informed decisions and planning their arrival to be able to choose parking locations with higher availability. Therefore, the aim of this study is to provide short-term predictions of available parking spaces with a low volume of data. The open parking lot provides parking spaces free of charge and one such parking lot, located beside a shopping center, was selected for this study. Parking availability data for 21 days was collected where 19 days were used for training, while multiple periods of the remaining 2 days were used to test and evaluate the prediction methods. The test dataset consists of data from a weekday and a weekend. Based on the reviewed literature, three prediction methods suitable for short-term prediction were selected, namely, Long-short term memory (LSTM), Seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), and the Ensemble-based method. TheLSTM method is a deep learning-based method, while SARIMAX is a regression-based method, and the Ensemble method is based on decision trees and random forest to provide predictions. The performance of the three prediction methods with low volume of data and the use of visitor trends data as an exogenous variable was evaluated. Based on the test prediction results, the LSTM and Ensemble-based methods provided better short-term predictions at multiple times on a weekday, while the Ensemble-based method provided better predictions over the weekend. However, the use of visitor trend data did not facilitate improving the predictions of SARIMAX and the Ensemble-based method, while it improved the LSTM prediction for the weekend.

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  • 45.
    Pettersson, Kim
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Android Messaging Investigator: Forensic text analysis tool for SMS, Kik and Hangouts conversations2018Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    With increasing usage of smartphones and text messaging through SMS and instant messaging applications, comes an increased need to be able to effectively analyze textual content in a forensic manner. Many of the common forensic applications relies on simple key word searching and regular expressions to find potential evidence in smartphones.

    In this report a forensic analysis application for Android SMS, Kik and Hangouts conversations named Android Messaging Investigator (AMINV) is proposed.

    The application aims to make the forensic analysis more effective by providing the forensic investigator with sentiment analysis technology. A message written with a negative mood might contain threats or harassments that may be of interest during a forensic investigation.

    Visualization is offered to make it possible for the forensic investigator to gain an extensive visual overview over conversation and sentiment data. Common forensic software features as being able to create forensic cases, search and filter data, save evidence, validate input data through hash-sums and generating forensic reports are also provided.

    The report covers the whole software implementation process. The final results are the developed forensic application, performance measurements of the sentiment analysis tool, a general performance analysis and an extensive stress-test.

  • 46.
    Prochazka, Jiri
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Zerin, Sonia
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Modeling Hospital Readmissions in Dalarna County, Sweden2016Självständigt arbete på avancerad nivå (masterexamen), 20 poäng / 30 hpStudentuppsats (Examensarbete)
    Abstract [en]

    Unplanned hospital readmissions increase health and medical care costs and indicate lower the lower quality of the healthcare services. Hence, predicting patients at risk to be readmitted is of interest. Using administrative data of patients being treated in the medical centers and hospitals in the Dalarna County, Sweden, during 2008 – 2016 two risk prediction models of hospital readmission are built. The first model relies on the logistic regression (LR) approach, predicts correctly 2,648 out of 3,392 observed readmission in the test dataset, reaching a c-statistics of 0.69. The second model is built using random forests (RF) algorithm; correctly predicts 2,183 readmission (out of 3,366) and 13,198 non-readmission events (out of 18,982). The discriminating ability of the best performing RF model (c-statistic 0.60) is comparable to that of the logistic model. Although the discriminating ability of both LR and RF risk prediction models is relatively modest, still these models are capable to identify patients running high risk of hospital readmission. These patients can then be targeted with specific interventions, in order to prevent the readmission, improve patients’ quality of life and reduce health and medical care costs.

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  • 47.
    Rebreyend, Pascal
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Lemarchand, Laurent
    Euler, Reinhardt
    A computational comparison of different algorithms for very large p-median problems2015Ingår i: Evolutionary Computation in Combinatorial Optimization: 15th European Conference, EvoCOP 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings / [ed] Gabriela Ochoa, Francisco Chicano, Springer, 2015, Vol. 9026, s. 13-24Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper, we propose a new method for solving large scale p-median problem instances based on real data. We compare different approaches in terms of runtime, memory footprint and quality of solutions obtained. In order to test the different methods on real data, we introduce a new benchmark for the p-median problem based on real Swedish data. Because of the size of the problem addressed, up to 1938 candidate nodes, a number of algorithms, both exact and heuristic, are considered. We also propose an improved hybrid version of a genetic algorithm called impGA. Experiments show that impGA behaves as well as other methods for the standard set of medium-size problems taken from Beasley’s benchmark, but produces comparatively good results in terms of quality, runtime and memory footprint on our specific benchmark based on real Swedish data.

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  • 48.
    Rönningsberg, Olle
    et al.
    Högskolan Dalarna, Institutionen för information och teknik.
    ten Hove, Sander
    Högskolan Dalarna, Institutionen för information och teknik.
    COVID-19 and structural breaks: The case of the Swedish Housing Market2021Självständigt arbete på avancerad nivå (masterexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
    Abstract [en]

    This paper analyzes how the COVID-19 pandemic has affected the Swedish housing market, and in particular prices and shifts in trends. Different classes of housing objects in various counties are investigated. Combining web scraped housing data for the entirety of Sweden between 2016-01-01 and 2021-03-31, including economic, demographic, socioeconomic and locational data, a hedonic regression model is used to estimate how different variables influence the housing price. The base model is subsequently used to investigate if statistically significant structural breaks exist in relation to the COVID-19 pandemic for the different object types in the entire Swedish market and in certain specific counties. Structural breaks are found for the housing object types ‘Fritidshus’, ‘Lägenhet’ and ‘Radhus’ in the entire Swedish market and for “Villa” in Stockholm county shortly after the pandemic outbreak, suggesting there is evidence for a pandemic infused shift in housing price regime on the Swedish housing market for these object types in stated areas. Splitting the hedonic regression model into three, one pooled regression, one before and one after the identified breaks, and comparing the shifts in impact of the housing price determinants suggests different pandemic effects on different object types. The result indicates that for the object types ‘Lägenhet’ in the entire country and for ‘Villa’ in Stockholm county, living area has an increased impact on the price while the locational variable population density has a decreased impact after the breakpoint date compared to before. This could suggest that for permanent housing objects in these regions, living area might have become increasingly valued over location during the pandemic. The results further indicate the direct opposite effect on the shifted impact in living area and the population density for the price of the temporary housing type Fritidshus in entire Sweden. However, an indication for increased impact of the areas socioeconomic level is noted for all these three object types. These results hold as a ground for further research in the subject.

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  • 49.
    Sadeghian, Paria
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    A Multi-Dimensional Approach to Human Mobility and Transportation Mode Detection Using GPS Data2024Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    GPS tracking data is an essential resource for analyzing human travel patterns and evaluating the effects on transportation systems. The primary challenge, however, is to accurately identify the modes of transportation within unlabeled GPS data. These approaches range from simple rule-based systems to advanced machine-learning techniques. This dissertation aims to bridge this gap by examining the critical features and techniques of these methods and proposing a novel approach for detecting transportation modes in GPS tracking data. To achieve this goal, a comprehensive understanding of individual journeys is crucial. Thus, this research adopts a microdata analytic approach, encompassing data collection, processing, analysis, and decision-making stages. Doing so contributes to advancing human mobility research and transportation mode detection. 

    Paper I undertook a systematic review of transport mode detection methodologies to fill the research gap, emphasizing the predominance of supervised learning algorithms and highlighting the need for further research to address the limitations of small datasets. Paper II introduced a stepwise methodology, integrating unsupervised learning, GIS, and supervised algorithms to detect transport modes while minimizing reliance on labelled data. The Random Forest algorithm emerged as a precise but time-intensive solution. Paper III showcased a novel approach to transport mode detection using deep learning models, outperforming traditional machine learning methods. This paper signals the potential of deep learning in the field and demonstrates the importance of raw GPS data in enhancing accuracy. Paper V addressed the challenge of predicting human mobility patterns under the Hidden Markov Model (HMM) framework, highlighting the applicability of HMMs to understanding and predicting complex mobility behaviour. This paper emphasized the need for GPS tracking data in developing advanced mobility models. Paper IV ventured into hybrid methodology by combining K-means clustering with the ANP-PSO algorithm to enhance transportation mode classification. This pioneering approach improved classification accuracy while reducing dependence on labelled datasets. 

    Collectively, these papers underscore the opportunities and limitations in human mobility research, offering insights into future directions for mitigating data quality issues and improving the accuracy of transportation mode detection. These innovative methodologies have practical implications for transportation planning, resource allocation, and intelligent transportation system development, ultimately shaping the future of transportation research and decision-making. Standardized data collection, processing, and labelling methods are crucial and need attention in future research. Future research can focus on developing such benchmarks and validation protocols to enhance the reliability and comparability of results.

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  • 50.
    Sadeghian, Paria
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Human mobility behavior: Transport mode detection by GPS data2021Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    GPS tracking data are widely used to understand human travel behavior and to evaluate the impact of travel. A major advantage with the usage of GPS tracking devices for collecting data is that it enables the researcher to collect large amounts of highly accurate and detailed human mobility data. However, unlabeled GPS tracking data does not easily lend itself to detecting transportation mode and this has given rise to a range of methods and algorithms for this purpose. The algorithms used vary in design and functionality, from defining specific rules to advanced machine learning algorithms. There is however no previous comprehensive review of these algorithms and this thesis aims to identify their essential features and methods and to develop and demonstrate a method for the detection of transport mode in GPS tracking data. To do this, it is necessary to have a detailed description of the particular journey undertaken by an individual. Therefore, as part of the investigation, a microdata analytic approach is applied to the problem areas, including the stages of data collection, data processing, analyzing the data, and decision making.

    In order to fill the research gap, Paper I consists of a systematic literature review of the methods and essential features used for detecting the transport mode in unlabeled GPS tracking data. Selected empirical studies were categorized into rule-based methods, statistical methods, and machine learning methods. The evaluation shows that machine learning algorithms are the most common. In the evaluation, I compared the methods previously used, extracted features, types of dataset, and model accuracy of transport mode detection. The results show that there is no standard method used in transport mode detection. In the light of these results, I propose in Paper II a stepwise methodology to detect five transport modes taking advantage of the unlabeled GPS data by first using an unsupervised algorithm to detect the five transport modes. A GIS multi-criteria process was applied to label part of the dataset. The performance of the five supervised algorithms was evaluated by applying them to different portions of the labeled dataset. The results show that stepwise methodology can achieve high accuracy in detecting the transport mode by labeling only 10% of the data from the entire dataset. 

    For the future, one interesting area to explore would be the application of the stepwise methodology to a balanced and larger dataset. A semi-supervised deep-learning approach is suggested for development in transport mode detection, since this method can detect transport modes with only small amounts of labeled data. Thus, the stepwise methodology can be improved upon for further studies. 

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