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Järpe, Eric
Publications (10 of 15) Show all publications
Järpe, E. (2019). Visit to care center Angeles Cobo Lopez, Alcaudete, Andalucia, Spain: A secondment within the REMIND project.
Open this publication in new window or tab >>Visit to care center Angeles Cobo Lopez, Alcaudete, Andalucia, Spain: A secondment within the REMIND project
2019 (English)Report (Other (popular science, discussion, etc.))
Publisher
p. 20
National Category
Gerontology, specialising in Medical and Health Sciences Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:hh:diva-39442 (URN)
Available from: 2019-05-22 Created: 2019-05-22 Last updated: 2019-05-27Bibliographically approved
Ali Hamad, R., Järpe, E. & Lundström, J. (2018). Stability analysis of the t-SNE algorithm for human activity pattern data. In: : . Paper presented at The 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan, Oct. 7-10, 2018.
Open this publication in new window or tab >>Stability analysis of the t-SNE algorithm for human activity pattern data
2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

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

National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-38442 (URN)
Conference
The 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan, Oct. 7-10, 2018
Projects
SA3L
Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-01-11Bibliographically approved
Weckstén, M., Frick, J., Sjostrom, A. & Järpe, E. (2016). A Novel Method for Recovery from Crypto Ransomware Infections. In: 2016 2nd IEEE International Conference on Computer and Communications, ICCC 2016 - Proceedings: . Paper presented at 2nd IEEE International Conference on Computer and Communications (ICCC), Oct 14-17, 2016, Chengdu, China (pp. 1354-1358). New York: IEEE
Open this publication in new window or tab >>A Novel Method for Recovery from Crypto Ransomware Infections
2016 (English)In: 2016 2nd IEEE International Conference on Computer and Communications, ICCC 2016 - Proceedings, New York: IEEE, 2016, p. 1354-1358Conference paper, Published paper (Refereed)
Abstract [en]

Extortion using digital platforms is an increasing form of crime. A commonly seen problem is extortion in the form of an infection of a Crypto Ransomware that encrypts the files of the target and demands a ransom to recover the locked data. By analyzing the four most common Crypto Ransomwares, at writing, a clear vulnerability is identified; all infections rely on tools available on the target system to be able to prevent a simple recovery after the attack has been detected. By renaming the system tool that handles shadow copies it is possible to recover from infections from all four of the most common Crypto Ransomwares. The solution is packaged in a single, easy to use script. © 2016 IEEE.

Place, publisher, year, edition, pages
New York: IEEE, 2016
Series
IEEE International Conference on Computer Communications and Networks, ISSN 1095-2055
Keywords
component, crypto ransom ware, malware, recovery, extortion, network security
National Category
Embedded Systems
Identifiers
urn:nbn:se:hh:diva-35642 (URN)10.1109/CompComm.2016.7924925 (DOI)000411576802046 ()2-s2.0-85020228603 (Scopus ID)978-1-4673-9026-2 (ISBN)
Conference
2nd IEEE International Conference on Computer and Communications (ICCC), Oct 14-17, 2016, Chengdu, China
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2018-08-28Bibliographically approved
Lundström, J., Järpe, E. & Verikas, A. (2016). Detecting and exploring deviating behaviour of smart home residents. Expert systems with applications, 55, 429-440
Open this publication in new window or tab >>Detecting and exploring deviating behaviour of smart home residents
2016 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 55, p. 429-440Article in journal (Refereed) Published
Abstract [en]

A system for detecting deviating human behaviour in a smart home environment is the long-term goal of this work. Clearly, such systems will be very important in ambient assisted living services. A new approach to modelling human behaviour patterns is suggested in this paper. The approach reveals promising results in unsupervised modelling of human behaviour and detection of deviations by using such a model. Human behaviour/activity in a short time interval is represented in a novel fashion by responses of simple non-intrusive sensors. Deviating behaviour is revealed through data clustering and analysis of associations between clusters and data vectors representing adjacent time intervals (analysing transitions between clusters). To obtain clusters of human behaviour patterns, first, a random forest is trained without using beforehand defined teacher signals. Then information collected in the random forest data proximity matrix is mapped onto the 2D space and data clusters are revealed there by agglomerative clustering. Transitions between clusters are modelled by the third order Markov chain.

Three types of deviations are considered: deviation in time, deviation in space and deviation in the transition between clusters of similar behaviour patterns.

The proposed modelling approach does not make any assumptions about the position, type, and relationship of sensors but is nevertheless able to successfully create and use a model for deviation detection-this is claimed as a significant result in the area of expert and intelligent systems. Results show that spatial and temporal deviations can be revealed through analysis of a 2D map of high dimensional data. It is demonstrated that such a map is stable in terms of the number of clusters formed. We show that the data clusters can be understood/explored by finding the most important variables and by analysing the structure of the most representative tree. © 2016 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2016
Keywords
Ambient assisted living, Random forests, Stochastic neighbour embedding, Markov chain, Intelligent environments
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Signal Processing
Identifiers
urn:nbn:se:hh:diva-30594 (URN)10.1016/j.eswa.2016.02.030 (DOI)000374811000033 ()2-s2.0-84960082873 (Scopus ID)
Projects
CAISR / SA3L
Funder
Knowledge Foundation, 2010/0271
Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2018-03-22Bibliographically approved
Lundström, J., Synnott, J., Järpe, E. & Nugent, C. (2015). Smart Home Simulation using Avatar Control and Probabilistic Sampling. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops): . Paper presented at SmartE: Closing the Loop – The 2nd IEEE PerCom Workshop on Smart Environments, St. Louis, Missouri, USA, March 23-27, 2015 (pp. 336-341). Piscataway, NJ: IEEE Press
Open this publication in new window or tab >>Smart Home Simulation using Avatar Control and Probabilistic Sampling
2015 (English)In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), Piscataway, NJ: IEEE Press, 2015, p. 336-341Conference paper, Published paper (Refereed)
Abstract [en]

Development, testing and validation of algorithms for smart home applications are often complex, expensive and tedious processes. Research on simulation of resident activity patterns in Smart Homes is an active research area and facilitates development of algorithms of smart home applications. However, the simulation of passive infrared (PIR) sensors is often used in a static fashion by generating equidistant events while an intended occupant is within sensor proximity. This paper suggests the combination of avatar-based control and probabilistic sampling in order to increase realism of the simulated data. The number of PIR events during a time interval is assumed to be Poisson distributed and this assumption is used in the simulation of Smart Home data. Results suggest that the proposed approach increase realism of simulated data, however results also indicate that improvements could be achieved using the geometric distribution as a model for the number of PIR events during a time interval. © IEEE 2015

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2015
Keywords
Avatars, Intelligent sensors, Smart homes, Data models, Software, Conferences
National Category
Signal Processing Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-27741 (URN)10.1109/PERCOMW.2015.7134059 (DOI)000380510900076 ()2-s2.0-84946097507 (Scopus ID)978-1-4799-8425-1 (ISBN)
Conference
SmartE: Closing the Loop – The 2nd IEEE PerCom Workshop on Smart Environments, St. Louis, Missouri, USA, March 23-27, 2015
Projects
SA3L, CAISR
Funder
Knowledge Foundation
Note

This work was supported by the Knowledge Foundation of Sweden, Grant Number 2010/0271. Additionally, Invest Northern Ireland is acknowledged for supporting this project under the Competence Centre Program Grant RD0513853 - Connected Health Innovation Centre.

Available from: 2015-05-26 Created: 2015-02-09 Last updated: 2018-03-22Bibliographically approved
Rögnvaldsson, T., Norrman, H., Byttner, S. & Järpe, E. (2014). Estimating p-Values for Deviation Detection. In: Randall Bilof (Ed.), Proceedings: 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems SASO 2014. Paper presented at SASO 2014 - Eighth IEEE International Conference on Self-Adaptive and Self-Organizing Systems, Imperial College, London, United Kingdom, September 8-12, 2014 (pp. 100-109). Los Alamitos, CA: IEEE Computer Society
Open this publication in new window or tab >>Estimating p-Values for Deviation Detection
2014 (English)In: Proceedings: 2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems SASO 2014 / [ed] Randall Bilof, Los Alamitos, CA: IEEE Computer Society, 2014, p. 100-109Conference paper, Published paper (Refereed)
Abstract [en]

Deviation detection is important for self-monitoring systems. To perform deviation detection well requires methods that, given only "normal" data from a distribution of unknown parametric form, can produce a reliable statistic for rejecting the null hypothesis, i.e. evidence for devating data. One measure of the strength of this evidence based on the data is the p-value, but few deviation detection methods utilize p-value estimation. We compare three methods that can be used to produce p-values: one class support vector machine (OCSVM), conformal anomaly detection (CAD), and a simple "most central pattern" (MCP) algorithm. The SVM and the CAD method should be able to handle a distribution of any shape. The methods are evaluated on synthetic data sets to test and illustrate their strengths and weaknesses, and on data from a real life self-monitoring scenario with a city bus fleet in normal traffic. The OCSVM has a Gaussian kernel for the synthetic data and a Hellinger kernel for the empirical data. The MCP method uses the Mahalanobis metric for the synthetic data and the Hellinger metric for the empirical data. The CAD uses the same metrics as the MCP method and has a k-nearest neighbour (kNN) non-conformity measure for both sets. The conclusion is that all three methods give reasonable, and quite similar, results on the real life data set but that they have clear strengths and weaknesses on the synthetic data sets. The MCP algorithm is quick and accurate when the "normal" data distribution is unimodal and symmetric (with the chosen metric) but not otherwise. The OCSVM is a bit cumbersome to use to create (quantized) p-values but is accurate and reliable when the data distribution is multimodal and asymmetric. The CAD is also accurate for multimodal and asymmetric distributions. The experiment on the vehicle data illustrate how algorithms like these can be used in a self-monitoring system that uses a fleet of vehicles to conduct deviation detection without supervisi- n and without prior knowledge about what is being monitored. © 2014 IEEE.

Place, publisher, year, edition, pages
Los Alamitos, CA: IEEE Computer Society, 2014
Series
International Conference on Self-Adaptive and Self-Organizing Systems : [proceedings], ISSN 1949-3673
Keywords
Training, Kernel, Vehicles, Conferences, Histograms, Design automation, Measurement
National Category
Computer Systems
Identifiers
urn:nbn:se:hh:diva-26151 (URN)10.1109/SASO.2014.22 (DOI)000361021200011 ()2-s2.0-84936889577 (Scopus ID)978-1-4799-5367-7 (ISBN)978-1-4799-5368-4 (ISBN)
Conference
SASO 2014 - Eighth IEEE International Conference on Self-Adaptive and Self-Organizing Systems, Imperial College, London, United Kingdom, September 8-12, 2014
Funder
VINNOVA
Note

Funding: Vinnova & Volvo AB

Available from: 2014-07-15 Created: 2014-07-15 Last updated: 2018-03-22Bibliographically approved
Järpe, E. (2013). Räkna med rester: Matematik att tillämpas inom kryptologi. Lund: Studentlitteratur
Open this publication in new window or tab >>Räkna med rester: Matematik att tillämpas inom kryptologi
2013 (Swedish)Book (Other academic)
Abstract [sv]

Ämnet kryptologi, dvs. kryptering, dekryptering och kodknäckning ­omfattar såväl matematik, dator­programmering som allmän finurlighet. Denna bok behandlar Caesarkrypto, substitutionskrypto, Vignèrekrypto, RSA-krypto och den bakom­liggande ­matematiken (ekvations­lösning, räkning med exponential­uttryck, resträkning, primtalsteori och rekursion) samt angränsande matematik (såsom kombinatorik, statisti­ska metoder för t.ex. detektering av krypterad kod och beräkning av överföringskvalitet). Boken innehåller även en så pass bred ­genomgång av elementär algebra (mängdlära, logik, trigono­metri, komplexa tal och rekurrensekva­tioner) och analys (funktioner i en variabel, derivata och integraler) att den kan användas vid inledande studier i matematik inom en mängd olika utbildningar. Boken syftar till att ge en introduktion till kryptologisk problem­lösning och visa på de stora synergieffekter som uppnås genom att tillämpa en väl­balanserad ­kombination av grundläggande matematik och elementär programmering inom området. Förhoppningen är också att läsaren, sporrad av de nyvunna insikterna om kryptologi, lockas till vidare kunskapsfördjupning. Boken vänder sig i första hand till blivande IT-forensiker som kan behöva kompetensen att kryptera och knäcka krypton i sin yrkesroll men som inte har en omfattande matematisk förkunskap. Den ­vänder sig även till studenter på landets tekniska högskolor och ­universitet.

Place, publisher, year, edition, pages
Lund: Studentlitteratur, 2013. p. 302
Keywords
Kryptering, Kryptologi
National Category
Mathematical Analysis Probability Theory and Statistics
Identifiers
urn:nbn:se:hh:diva-22945 (URN)978-91-44-08870-9 (ISBN)
Available from: 2013-06-18 Created: 2013-06-18 Last updated: 2018-03-22Bibliographically approved
Nilsson, E., Nilsson, B. & Järpe, E. (2011). A Pharmaceutical Anti-counterfeiting Method Using Time Controlled Numeric Tokens. In: 2011 IEEE International Conference on RFID-Technologies and Applications. Paper presented at 2011 IEEE International Conference on RFID-Technologies and Applications (pp. 335-339). Piscataway, NJ: IEEE Press
Open this publication in new window or tab >>A Pharmaceutical Anti-counterfeiting Method Using Time Controlled Numeric Tokens
2011 (English)In: 2011 IEEE International Conference on RFID-Technologies and Applications, Piscataway, NJ: IEEE Press, 2011, p. 335-339Conference paper, Published paper (Refereed)
Abstract [en]

An anti-counterfeit and authentication method usingtime controlled numeric tokens enabling a secure logistic chain ispresented. Implementation of the method is illustrated with apharmaceutical anti-counterfeit system. The method uses activeRFID technology in combination with product seal. Authenticityis verified by comparing time controlled ID-codes, i.e. numerictokens, stored in RFID tags and by identical numeric tokensstored in a secure database. The pharmaceutical products areprotected from the supplier to the pharmacist, with thepossibility to extend the authentication out to the end customer.The ability of the method is analyzed by discussion of severalpossible scenarios. It is shown that an accuracy of 99.9% tellingthe customer she has an authentic product is achieved by the useof 11-bit ID-code strings.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2011
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-16341 (URN)10.1109/RFID-TA.2011.6068659 (DOI)2-s2.0-82155181989 (Scopus ID)978-1-4577-0028-6 (ISBN)
Conference
2011 IEEE International Conference on RFID-Technologies and Applications
Available from: 2011-09-27 Created: 2011-09-26 Last updated: 2018-03-22Bibliographically approved
Sandberg, M., Florén, H., Halila, F., Hörte, S.-Å. & Järpe, E. (2011). Evolution of Green Innovation in Sweden: Models, Management, Policies. In: Evolution of Green Innovation in Sweden: Models, Management, Policies. Paper presented at 2nd International Conference on Sustainability Transitions, June 13-15, Lund, Lund University, Sweden.
Open this publication in new window or tab >>Evolution of Green Innovation in Sweden: Models, Management, Policies
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2011 (English)In: Evolution of Green Innovation in Sweden: Models, Management, Policies, 2011Conference paper, Published paper (Other academic)
Abstract [en]

Quantitative analysis of the evolution of innovations at national systems level is not alwayspossible due to the lack of reliable, comprehensive and adequate data sets. Therefore, managerialpractice among organizations as well as policy decision making are often myopic anduninformed about actual dynamics.In the Swedish case, there are promising data sets, even if the adequacy of existing variabledefinitions needs to be explored and debated. Official data collected by the central statisticsauthority SCB (Statistics Sweden) includes several potentially relevant variables on all privateand public organizations in Sweden and their employees. These data are compiled into timeseries for a number of years which allows for longitudinal analysis. Data can also be mergedwith other data sets on the environmental goods and services sector and energy consumption dataand therefore allow for a detailed “demographic” or “population ecology” analysis ofenvironmentally oriented or friendly innovation since at least 2003. Halmstad University hasrecently gained full access to these data.In this paper, these databases are described in some detail. Problems of definitions andmeasurement are particularly discussed, and some initial descriptive statistics are presented.Further, the paper advocates the use of models inspired by population ecology and demographyin analyzing existing data. In particular it is suggested that interactive diffusion models mayenhance the understanding of the evolution of green innovations and their dynamics. It is alsosuggested that multi-level regression analysis is applicable in estimating the power of factors thatbring progress to the “greening” of the Swedish innovation system.Together, such models are potentially useful in forecasting the development of innovationsystems. The models can also be used in generating, testing by simulating and thus evaluatingapproaches to management of innovation and innovation policy implementation. A dynamicunderstanding of the “greening” of the innovation system is a critical asset in the development oftools to be used for continuous improvements in both policy making and the management ofinnovation in organizations.

Keywords
innovation, environment, data, Sweden
National Category
Social Sciences
Identifiers
urn:nbn:se:hh:diva-15434 (URN)
Conference
2nd International Conference on Sustainability Transitions, June 13-15, Lund, Lund University, Sweden
Available from: 2011-06-16 Created: 2011-06-16 Last updated: 2018-03-22Bibliographically approved
Sandberg, M., Florén, H. & Järpe, E. (2011). The Greening of the Swedish Innovation System: Exploring Official Registry Data. Paper presented at EAEPE (European Association for Evolutionary Political Economy) Annual Conference 2011 "Schumpeter´s Heritage - The Evolution of the Theory of Evolution", Vienna, Austria, October 27th – 30th, 2011.
Open this publication in new window or tab >>The Greening of the Swedish Innovation System: Exploring Official Registry Data
2011 (English)Conference paper, Published paper (Other academic)
Abstract [en]

Most countries aim to transform towards becoming greener societies. In parallel, many companies struggle with the question of how to build more sustainable operations while at the same time sustaining or developing their competitive advantage.  Research has, up until today, however, largely failed to provide solid explanations for how to achieve these aims, from which policy and managerial decision-making can deduced. One reason for this failure is that quantitative analysis of “green” innovation at national systems level is not always possible due to the lack of reliable, comprehensive and adequate data sets. In the Swedish case, there are promising data sets, even if one always can debate the adequacy of existing variable definitions. Official data collected by Statistics Sweden (SCB) includes several interesting variables on all private and public organizations in Sweden and all employees, compiled into time series for a number of years. These can be merged with other data sets on the environmental goods and services sector and energy consumption data and therefore allow for a detailed “demographic” or “population ecology” analysis of environmentally oriented or friendly innovation since at least 2003. In this paper, these databases are described in some detail. Problems of definitions and measurement are particularly discussed. Initial explorations describe the shift from fossil to non-fossil energy sources in the Swedish innovation system. Further, we also suggest some models inspired by demography and population ecology and also multi-level models. In particular it is suggested that diffusion models could be applied, including models in which diffusion processes interact in micro-level systems. It is suggested to apply multi-level regression analysis in order to estimate the power of factors affecting the “greening” of Swedish innovation system.

National Category
Business Administration Political Science (excluding Public Administration Studies and Globalisation Studies)
Identifiers
urn:nbn:se:hh:diva-16930 (URN)
Conference
EAEPE (European Association for Evolutionary Political Economy) Annual Conference 2011 "Schumpeter´s Heritage - The Evolution of the Theory of Evolution", Vienna, Austria, October 27th – 30th, 2011
Available from: 2012-01-16 Created: 2012-01-16 Last updated: 2018-03-22Bibliographically approved
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