Ändra sökning
Avgränsa sökresultatet
1 - 22 av 22
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Träffar per sida
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
  • Standard (Relevans)
  • Författare A-Ö
  • Författare Ö-A
  • Titel A-Ö
  • Titel Ö-A
  • Publikationstyp A-Ö
  • Publikationstyp Ö-A
  • Äldst först
  • Nyast först
  • Skapad (Äldst först)
  • Skapad (Nyast först)
  • Senast uppdaterad (Äldst först)
  • Senast uppdaterad (Nyast först)
  • Disputationsdatum (tidigaste först)
  • Disputationsdatum (senaste först)
Markera
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Anton, Borg
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Clustering residential burglaries using multiple heterogeneous variablesIngår i: International Journal of Information Technology & Decision MakingArtikel i tidskrift (Refereegranskat)
    Abstract [en]

    To identify series of residential burglaries, detecting linked crimes performed bythe same constellations of criminals is necessary. Comparison of crime reports today isdicult as crime reports traditionally have been written as unstructured text and oftenlack a common information-basis. Based on a novel process for collecting structured crimescene information the present study investigates the use of clustering algorithms to groupsimilar crime reports based on combined crime characteristics from the structured form.Clustering quality is measured using Connectivity and Silhouette index, stability usingJaccard index, and accuracy is measured using Rand index and a Series Rand index.The performance of clustering using combined characteristics was compared with spatialcharacteristic. The results suggest that the combined characteristics perform better orsimilar to the spatial characteristic. In terms of practical signicance, the presentedclustering approach is capable of clustering cases using a broader decision basis.

  • 2.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Multi-expert estimations of burglars' risk exposure and level of pre-crime preparation using coded crime scene data: Work in progress2018Ingår i: Proceedings - 2018 European Intelligence and Security Informatics Conference, EISIC 2018 / [ed] Brynielsson, J, Institute of Electrical and Electronics Engineers Inc. , 2018, s. 77-80Konferensbidrag (Refereegranskat)
    Abstract [en]

    Law enforcement agencies strive to link crimes perpetrated by the same offenders into crime series in order to improve investigation efficiency. Such crime linkage can be done using both physical traces (e.g., DNA or fingerprints) or 'soft evidence' in the form of offenders' modus operandi (MO), i.e. their behaviors during crimes. However, physical traces are only present for a fraction of crimes, unlike behavioral evidence. This work-in-progress paper presents a method for aggregating multiple criminal profilers' ratings of offenders' behavioral characteristics based on feature-rich crime scene descriptions. The method calculates consensus ratings from individual experts' ratings, which then are used as a basis for classification algorithms. The classification algorithms can automatically generalize offenders' behavioral characteristics from cues in the crime scene data. Models trained on the consensus rating are evaluated against models trained on individual profiler's ratings. Thus, whether the consensus model shows improved performance over individual models. © 2018 IEEE.

  • 3.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    A statistical method for detecting significant temporal hotspots using LISA statistics2017Ingår i: Proceedings - 2017 European Intelligence and Security Informatics Conference, EISIC 2017, IEEE Computer Society, 2017, s. 123-126Konferensbidrag (Refereegranskat)
    Abstract [en]

    This work presents a method for detecting statisticallysignificant temporal hotspots, i.e. the date and time of events,which is useful for improved planning of response activities.Temporal hotspots are calculated using Local Indicators ofSpatial Association (LISA) statistics. The temporal data is ina 7x24 matrix that represents a temporal resolution of weekdaysand hours-in-the-day. Swedish residential burglary events areused in this work for testing the temporal hotspot detectionapproach. Although, the presented method is also useful forother events as long as they contain temporal information, e.g.attack attempts recorded by intrusion detection systems. Byusing the method for detecting significant temporal hotspotsit is possible for domain-experts to gain knowledge about thetemporal distribution of the events, and also to learn at whichtimes mitigating actions could be implemented.

  • 4. Boldt, Martin
    et al.
    Borg, Anton
    Carlsson, Bengt
    On the Simulation of a Software Reputation System2010Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, there are difficulties finding all malicious programs due to juridical restrictions and deficits concerning the anti-malicious programs. Also, a "grey-zone" of questionable programs exists, hard for different protection programs to handle and almost impossible for a single user to judge. A software reputation system consisting of expert, average and novice users are proposed as a complement to let anti-malware programs or dedicated human experts decide about questionable programs. A simulation of the factors involved is accomplished by varying the user groups involved, modifying each user's individual trust factor, specifying an upper trust factor limit and accounting for previous rating influence. As a proposed result, a balanced, well-informed rating of judged programs appears, i.e. a balance between quickly reaching a well-informed decision and not giving a single voter too much power.

  • 5.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Ickin, Selim
    Ericsson Research, SWE.
    Gustafsson, Jörgen
    Ericsson Research, SWE.
    Anomaly detection of event sequences using multiple temporal resolutions and Markov chains2019Ingår i: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Streaming data services, such as video-on-demand, are getting increasingly more popular, and they are expected to account for more than 80% of all Internet traffic in 2020. In this context, it is important for streaming service providers to detect deviations in service requests due to issues or changing end-user behaviors in order to ensure that end-users experience high quality in the provided service. Therefore, in this study we investigate to what extent sequence-based Markov models can be used for anomaly detection by means of the end-users’ control sequences in the video streams, i.e., event sequences such as play, pause, resume and stop. This anomaly detection approach is further investigated over three different temporal resolutions in the data, more specifically: 1 h, 1 day and 3 days. The proposed anomaly detection approach supports anomaly detection in ongoing streaming sessions as it recalculates the probability for a specific session to be anomalous for each new streaming control event that is received. Two experiments are used for measuring the potential of the approach, which gives promising results in terms of precision, recall, F 1 -score and Jaccard index when compared to k-means clustering of the sessions. © 2019, The Author(s).

  • 6.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Melander, Ulf
    En strukturerad metod för registrering och automatisk analys av brott2014Ingår i: The Past, the Present and the Future of Police Research: Proceedings from the fifth Nordic Police Research seminar / [ed] Rolf Granér och Ola Kronkvist, 2014Konferensbidrag (Refereegranskat)
    Abstract [sv]

    I detta artikel beskrivs en metod som används i polisregionerna Syd, Väst och Stockholm1 för att samla in strukturerade brottsplatsuppgifter från bostadsinbrott, samt hur den insamlade informationen kan analyseras med automatiska metoder som kan assistera brottssamordnare i deras arbete. Dessa automatiserade analyser kan användas som filtrerings- eller selekteringsverktyg för bostadsinbrott och därmed effektivisera och underlätta arbetet. Vidare kan metoden användas för att avgöra sannolikheten att två brott är utförda av samma gärningsman, vilket kan hjälpa polisen att identifiera serier av brott. Detta är möjligt då gärningsmän tenderar att begå brott på ett snarlikt sätt och det är möjligt, baserat på strukturerade brottsplatsuppgifter, att automatiskt hitta dessa mönster. I kapitlet presenteras och utvärderas en prototyp på ett IT-baserat beslutsstödsystem samt två automatiska metoder för brottssamordning.

  • 7.
    Boldt, Martin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Svensson, Martin
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för industriell ekonomi.
    Hildeby, Jonas
    Polisen, SWE.
    Predicting burglars' risk exposure and level of pre-crime preparation using crime scene data2018Ingår i: Intelligent Data Analysis, ISSN 1088-467X, Vol. 22, nr 1, s. 167-190, artikel-id IDA 322-3210Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Objectives: The present study aims to extend current research on how offenders’ modus operandi (MO) can be used in crime linkage, by investigating the possibility to automatically estimate offenders’ risk exposure and level of pre-crime preparation for residential burglaries. Such estimations can assist law enforcement agencies when linking crimes into series and thus provide a more comprehensive understanding of offenders and targets, based on the combined knowledge and evidence collected from different crime scenes. Methods: Two criminal profilers manually rated offenders’ risk exposure and level of pre-crime preparation for 50 burglaries each. In an experiment we then analyzed to what extent 16 machine-learning algorithms could generalize both offenders’ risk exposure and preparation scores from the criminal profilers’ ratings onto 15,598 residential burglaries. All included burglaries contain structured and feature-rich crime descriptions which learning algorithms can use to generalize offenders’ risk and preparation scores from.Results: Two models created by Naïve Bayes-based algorithms showed best performance with an AUC of 0.79 and 0.77 for estimating offenders' risk and preparation scores respectively. These algorithms were significantly better than most, but not all, algorithms. Both scores showed promising distinctiveness between linked series, as well as consistency for crimes within series compared to randomly sampled crimes.Conclusions: Estimating offenders' risk exposure and pre-crime preparation  can complement traditional MO characteristics in the crime linkage process. The estimations are also indicative to function for cross-category crimes that otherwise lack comparable MO. Future work could focus on increasing the number of manually rated offenses as well as fine-tuning the Naïve Bayes algorithm to increase its estimation performance.

  • 8.
    Borg, Anton
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Decision Support for Estimation of the Utility of Software and E-mail2012Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Background: Computer users often need to distinguish between good and bad instances of software and e-mail messages without the aid of experts. This decision process is further complicated as the perception of spam and spyware varies between individuals. As a consequence, users can benefit from using a decision support system to make informed decisions concerning whether an instance is good or bad. Objective: This thesis investigates approaches for estimating the utility of e-mail and software. These approaches can be used in a personalized decision support system. The research investigates the performance and accuracy of the approaches. Method: The scope of the research is limited to the legal grey- zone of software and e-mail messages. Experimental data have been collected from academia and industry. The research methods used in this thesis are simulation and experimentation. The processing of user input, along with malicious user input, in a reputation system for software were investigated using simulations. The preprocessing optimization of end user license agreement classification was investigated using experimentation. The impact of social interaction data in regards to personalized e-mail classification was also investigated using experimentation. Results: Three approaches were investigated that could be adapted for a decision support system. The results of the investigated reputation system suggested that the system is capable, on average, of producing a rating ±1 from an objects correct rating. The results of the preprocessing optimization of end user license agreement classification suggested negligible impact. The results of using social interaction information in e-mail classification suggested that accurate spam detectors can be generated from the low-dimensional social data model alone, however, spam detectors generated from combinations of the traditional and social models were more accurate. Conclusions: The results of the presented approaches suggestthat it is possible to provide decision support for detecting software that might be of low utility to users. The labeling of instances of software and e-mail messages that are in a legal grey-zone can assist users in avoiding an instance of low utility, e.g. spam and spyware. A limitation in the approaches is that isolated implementations will yield unsatisfactory results in a real world setting. A combination of the approaches, e.g. to determine the utility of software, could yield improved results.

  • 9.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    On Descriptive and Predictive Models for Serial Crime Analysis2014Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    Law enforcement agencies regularly collect crime scene information. There exists, however, no detailed, systematic procedure for this. The data collected is affected by the experience or current condition of law enforcement officers. Consequently, the data collected might differ vastly between crime scenes. This is especially problematic when investigating volume crimes. Law enforcement officers regularly do manual comparison on crimes based on the collected data. This is a time-consuming process; especially as the collected crime scene information might not always be comparable. The structuring of data and introduction of automatic comparison systems could benefit the investigation process. This thesis investigates descriptive and predictive models for automatic comparison of crime scene data with the purpose of aiding law enforcement investigations. The thesis first investigates predictive and descriptive methods, with a focus on data structuring, comparison, and evaluation of methods. The knowledge is then applied to the domain of crime scene analysis, with a focus on detecting serial residential burglaries. This thesis introduces a procedure for systematic collection of crime scene information. The thesis also investigates impact and relationship between crime scene characteristics and how to evaluate the descriptive model results. The results suggest that the use of descriptive and predictive models can provide feedback for crime scene analysis that allows a more effective use of law enforcement resources. Using descriptive models based on crime characteristics, including Modus Operandi, allows law enforcement agents to filter cases intelligently. Further, by estimating the link probability between cases, law enforcement agents can focus on cases with higher link likelihood. This would allow a more effective use of law enforcement resources, potentially allowing an increase in clear-up rates.

  • 10.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information2016Ingår i: International Journal of Information Technology and Decision Making, ISSN 0219-6220, Vol. 15, nr 1, s. 23-42Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To identify series of residential burglaries, detecting linked crimes performed by the same constellations of criminals is necessary. Comparison of crime reports today is difficult as crime reports traditionally have been written as unstructured text and often lack a common information-basis. Based on a novel process for collecting structured crime scene information, the present study investigates the use of clustering algorithms to group similar crime reports based on combined crime characteristics from the structured form. Clustering quality is measured using Connectivity and Silhouette index (SI), stability using Jaccard index, and accuracy is measured using Rand index (RI) and a Series Rand index (SRI). The performance of clustering using combined characteristics was compared with spatial characteristic. The results suggest that the combined characteristics perform better or similar to the spatial characteristic. In terms of practical significance, the presented clustering approach is capable of clustering cases using a broader decision basis.

  • 11. Borg, Anton
    et al.
    Boldt, Martin
    Carlsson, Bengt
    Simulating malicious users in a software reputation system2011Ingår i: Communications in Computer and Information Science, Springer , 2011, Vol. 186, s. 147-156Konferensbidrag (Refereegranskat)
    Abstract [en]

    Today, computer users have trouble in separating malicious and legitimate software. Traditional countermeasures such as anti-virus tools mainly protect against truly malicious programs, but the situation is complicated due to a "grey-zone" of questionable programs that are difficult to classify. We therefore suggest a software reputation system (SRS) to help computer users in separating legitimate software from its counterparts. In this paper we simulate the usage of a SRS to investigate the effects that malicious users have on the system. Our results show that malicious users will have little impact on the overall system, if kept within 10% of the population. However, a coordinated attack against a selected subset of the applications may distort the reputation of these applications. The results also show that there are ways to detect attack attempts in an early stage. Our conclusion is that a SRS could be used as a decision support system to protect against questionable software.

  • 12.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Eliasson, Johan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Detecting Crime Series Based on Route Estimation and Behavioral Similarity2017Ingår i: 2017 EUROPEAN INTELLIGENCE AND SECURITY INFORMATICS CONFERENCE (EISIC) / [ed] Brynielsson, J, IEEE , 2017, s. 1-8Konferensbidrag (Refereegranskat)
    Abstract [en]

    A majority of crimes are committed by a minority of offenders. Previous research has provided some support for the theory that serial offenders leave behavioral traces on the crime scene which could be used to link crimes to serial offenders. The aim of this work is to investigate to what extent it is possible to use geographic route estimations and behavioral data to detect serial offenders. Experiments were conducted using behavioral data from authentic burglary reports to investigate if it was possible to find crime routes with high similarity. Further, the use of burglary reports from serial offenders to investigate to what extent it was possible to detect serial offender crime routes. The result show that crime series with the same offender on average had a higher behavioral similarity than random crime series. Sets of crimes with high similarity, but without a known offender would be interesting for law enforcement to investigate further. The algorithm is also evaluated on 9 crime series containing a maximum of 20 crimes per series. The results suggest that it is possible to detect crime series with high similarity using analysis of both geographic routes and behavioral data recorded at crime scenes.

  • 13.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Eliasson, Johan
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Detecting Crime Series Based on Route Estimationand Behavioral Similarity2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    A majority of crimes are committed by a minority of offenders. Previous research has provided some support for the theory that serial offenders leave behavioral traces on the crime scene which could be used to link crimes to serial offenders. The aim of this work is to investigate to what extent it is possible to use geographic route estimations and behavioral data to detect serial offenders. Experiments were conducted using behavioral data from authentic burglary reports to investigate if it was possible to find crime routes with high similarity. Further, the use of burglary reports from serial offenders to investigate to what extent it was possible to detect serial offender crime routes. The result show that crime series with the same offender on average had a higher behavioral similarity than random crime series. Sets of crimes with high similarity, but without a known offender would be interesting for law enforcement to investigate further. The algorithm is also evaluated on 9 crime series containing a maximum of 20 crimes per series. The results suggest that it is possible to detect crime series with high similarity using analysis of both geographic routes and behavioral data recorded at crime scenes.

  • 14. Borg, Anton
    et al.
    Boldt, Martin
    Lavesson, Niklas
    Informed Software Installation through License Agreement Categorization2011Konferensbidrag (Refereegranskat)
    Abstract [en]

    Spyware detection can be achieved by using machinelearning techniques that identify patterns in the End User License Agreements (EULAs) presented by application installers. However, solutions have required manual input from the user with varying degrees of accuracy. We have implemented an automatic prototype for extraction and classification and used it to generate a large data set of EULAs. This data set is used to compare four different machine learning algorithms when classifying EULAs. Furthermore, the effect of feature selection is investigated and for the top two algorithms, we investigate optimizing the performance using parameter tuning. Our conclusion is that feature selection and performance tuning are of limited use in this context, providing limited performance gains. However, both the Bagging and the Random Forest algorithms show promising results, with Bagging reaching an AUC measure of 0.997 and a False Negative Rate of 0.062. This shows the applicability of License Agreement Categorization for realizing informed software installation.

  • 15.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Melander, Ulf
    Boeva, Veselka
    Detecting serial residential burglaries using clustering2014Ingår i: Expert Systems with Applications, ISSN 0957-4174 , Vol. 41, nr 11, s. 5252-5266Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    According to the Swedish National Council for Crime Prevention, law enforcement agencies solved approximately three to five percent of the reported residential burglaries in 2012. Internationally, studies suggest that a large proportion of crimes are committed by a minority of offenders. Law enforcement agencies, consequently, are required to detect series of crimes, or linked crimes. Comparison of crime reports today is difficult as no systematic or structured way of reporting crimes exists, and no ability to search multiple crime reports exist. This study presents a systematic data collection method for residential burglaries. A decision support system for comparing and analysing residential burglaries is also presented. The decision support system consists of an advanced search tool and a plugin-based analytical framework. In order to find similar crimes, law enforcement officers have to review a large amount of crimes. The potential use of the cut-clustering algorithm to group crimes to reduce the amount of crimes to review for residential burglary analysis based on characteristics is investigated. The characteristics used are modus operandi, residential characteristics, stolen goods, spatial similarity, or temporal similarity. Clustering quality is measured using the modularity index and accuracy is measured using the rand index. The clustering solution with the best quality performance score were residential characteristics, spatial proximity, and modus operandi, suggesting that the choice of which characteristic to use when grouping crimes can positively affect the end result. The results suggest that a high quality clustering solution performs significantly better than a random guesser. In terms of practical significance, the presented clustering approach is capable of reduce the amounts of cases to review while keeping most connected cases. While the approach might miss some connections, it is also capable of suggesting new connections. The results also suggest that while crime series clustering is feasible, further investigation is needed.

  • 16.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Boldt, Martin
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Svensson, Johan
    Telenor Sverige AB, SWE.
    Using conformal prediction for multi-label document classification in e-Mail support systems2019Ingår i: Lect. Notes Comput. Sci., Springer Verlag , 2019, Vol. 11536, s. 308-322Konferensbidrag (Refereegranskat)
    Abstract [en]

    For any corporation the interaction with its customers is an important business process. This is especially the case for resolving various business-related issues that customers encounter. Classifying the type of such customer service e-mails to provide improved customer service is thus important. The classification of e-mails makes it possible to direct them to the most suitable handler within customer service. We have investigated the following two aspects of customer e-mail classification within a large Swedish corporation. First, whether a multi-label classifier can be introduced that performs similarly to an already existing multi-class classifier. Second, whether conformal prediction can be used to quantify the certainty of the predictions without loss in classification performance. Experiments were used to investigate these aspects using several evaluation metrics. The results show that for most evaluation metrics, there is no significant difference between multi-class and multi-label classifiers, except for Hamming loss where the multi-label approach performed with a lower loss. Further, the use of conformal prediction did not introduce any significant difference in classification performance for neither the multi-class nor the multi-label approach. As such, the results indicate that conformal prediction is a useful addition that quantifies the certainty of predictions without negative effects on the classification performance, which in turn allows detection of statistically significant predictions. © Springer Nature Switzerland AG 2019.

  • 17.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Lavesson, Niklas
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    E-mail Classification using Social Network Information2012Konferensbidrag (Refereegranskat)
    Abstract [en]

    A majority of E-mail is suspected to be spam. Traditional spam detection fails to differentiate between user needs and evolving social relationships. Online Social Networks (OSNs) contain more and more social information, contributed by users. OSN information may be used to improve spam detection. This paper presents a method that can use several social networks for detecting spam and a set of metrics for representing OSN data. The paper investigates the impact of using social network data extracted from an E-mail corpus to improve spam detection. The social data model is compared to traditional spam data models by generating and evaluating classifiers from both model types. The results show that accurate spam detectors can be generated from the low-dimensional social data model alone, however, spam detectors generated from combinations of the traditional and social models were more accurate than the detectors generated from either model in isolation.

  • 18.
    Borg, Anton
    et al.
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Lavesson, Niklas
    Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.
    Boeva, Veselka
    Comparison of clustering approaches for gene expression data2013Ingår i: Frontiers in Artificial Intelligence and Applications, IOS Press , 2013, Vol. 257, s. 55-64Konferensbidrag (Refereegranskat)
    Abstract [en]

    Clustering algorithms have been used to divide genes into groups according to the degree of their expression similarity. Such a grouping may suggest that the respective genes are correlated and/or co-regulated, and subsequently indicates that the genes could possibly share a common biological role. In this paper, four clustering algorithms are investigated: k-means, cut-clustering, spectral and expectation-maximization. The algorithms are benchmarked against each other. The performance of the four clustering algorithms is studied on time series expression data using Dynamic TimeWarping distance in order to measure similarity between gene expression profiles. Four different cluster validation measures are used to evaluate the clustering algorithms: Connectivity and Silhouette Index for estimating the quality of clusters, Jaccard Index for evaluating the stability of a cluster method and Rand Index for assessing the accuracy. The obtained results are analyzed by Friedman's test and the Nemenyi post-hoc test. K-means is demonstrated to be significantly better than the spectral clustering algorithm under the Silhouette and Rand validation indices.

  • 19.
    Erlandsson, Fredrik
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Johnson, Henric
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Bródka, Piotr
    Wrocław University of Technolog, POL.
    Predicting User Participation in Social Media2016Ingår i: Lecture Notes in Computer Science / [ed] Wierzbicki A., Brandes U., Schweitzer F., Pedreschi D., Springer, 2016, Vol. 9564, s. 126-135Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    Abstract Online social networking services like Facebook provides a popular way for users to participate in different communication groups and discuss relevant topics with each other. While users tend to have an impact on each other, it is important to better understand and ...

  • 20.
    Erlandsson, Fredrik
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Bródka, Piotr
    Wrocƚaw University of Technology, POL.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Seed selection for information cascade in multilayer networks2018Ingår i: Studies in Computational IntelligenceVolume , 2018, Pages -436 / [ed] Cherifi H.,Cherifi C.,Musolesi M.,Karsai M., Springer-Verlag New York, 2018, Vol. 689, s. 426-436Konferensbidrag (Refereegranskat)
    Abstract [en]

    Information spreading is an interesting field in the domain of online social media. In this work, we are investigating how well different seed selection strategies affect the spreading processes simulated using independent cascade model on eighteen multilayer social networks. Fifteen networks are built based on the user interaction data extracted from Facebook public pages and tree of them are multilayer networks downloaded from public repository (two of them being Twitter networks). The results indicate that various state of the art seed selection strategies for single-layer networks like K-Shell or VoteRank do not perform so well on multilayer networks and are outperformed by Degree Centrality.

  • 21.
    Erlandsson, Fredrik
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Bródka, Piotr
    Wrocƚaw University of Technology, POL.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Johnson, Henric
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Finding Influential Users in Social Media Using Association Rule Learning2016Ingår i: Entropy, ISSN 1099-4300, E-ISSN 1099-4300, Vol. 18, nr 5Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.

  • 22. Johansson, E.
    et al.
    Gahlin, C.
    Borg, Anton
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    Crime Hotspots: An Evaluation of the KDE Spatial Mapping Technique2015Ingår i: Proceedings - 2015 European Intelligence and Security Informatics Conference, EISIC 2015 / [ed] Brynielsson J.,Yap M.H., IEEE Computer Society, 2015, s. 69-74Konferensbidrag (Refereegranskat)
    Abstract [en]

    Residential burglaries are increasing. By visualizing patterns as spatial hotspots, law-enforcement agents can get a better understanding of crime distributions and trends. Two aspects are investigated, first, measuring the accuracy and performance of the KDE algorithm using small data sets. Secondly, investigation of the amount of crime data needed to compute accurate and reliable hotspots. The Prediction Accuracy Index is used to effectively measure the accuracy of the algorithm. The data from three geographical areas in Sweden, including Stockholm, Gothenburg and Malmö are analyzed and evaluated over a one year. The results suggest that the usage of the KDE algorithm to predict residential burglaries performs well overall when having access to enough crimes, but is capable with small data sets as well

1 - 22 av 22
RefereraExporteraLänk till träfflistan
Permanent länk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf