| 1. |
- Abdelraheem, Mohamed Ahmed, et al.
(författare)
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Executing Boolean queries on an encrypted Bitmap index
- 2016
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Ingår i: CCSW 2016 - Proceedings of the 2016 ACM Cloud Computing Security Workshop, co-located with CCS 2016. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450345729 ; , s. 11-22
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Konferensbidrag (refereegranskat)abstract
- We propose a simple and efficient searchable symmetric encryption scheme based on a Bitmap index that evaluates Boolean queries. Our scheme provides a practical solution in settings where communications and computations are very constrained as it offers a suitable trade-off between privacy and performance.
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| 2. |
- Abghari, Shahrooz, et al.
(författare)
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A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences
- 2018
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Ingår i: The 17th IEEE International Conference on Machine Learning and Applications Special Session on Machine Learning Algorithms, Systems and Applications. - : IEEE. ; , s. 1123-1130
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Konferensbidrag (refereegranskat)abstract
- Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.
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| 3. |
- Abghari, Shahrooz
(författare)
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Data Modeling for Outlier Detection
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis explores the data modeling for outlier detection techniques in three different application domains: maritime surveillance, district heating, and online media and sequence datasets. The proposed models are evaluated and validated under different experimental scenarios, taking into account specific characteristics and setups of the different domains.Outlier detection has been studied and applied in many domains. Outliers arise due to different reasons such as fraudulent activities, structural defects, health problems, and mechanical issues. The detection of outliers is a challenging task that can reveal system faults, fraud, and save people's lives. Outlier detection techniques are often domain-specific. The main challenge in outlier detection relates to modeling the normal behavior in order to identify abnormalities. The choice of model is important, i.e., an incorrect choice of data model can lead to poor results. This requires a good understanding and interpretation of the data, the constraints, and the requirements of the problem domain. Outlier detection is largely an unsupervised problem due to unavailability of labeled data and the fact that labeled data is expensive.We have studied and applied a combination of both machine learning and data mining techniques to build data-driven and domain-oriented outlier detection models. We have shown the importance of data preprocessing as well as feature selection in building suitable methods for data modeling. We have taken advantage of both supervised and unsupervised techniques to create hybrid methods. For example, we have proposed a rule-based outlier detection system based on open data for the maritime surveillance domain. Furthermore, we have combined cluster analysis and regression to identify manual changes in the heating systems at the building level. Sequential pattern mining for identifying contextual and collective outliers in online media data have also been exploited. In addition, we have proposed a minimum spanning tree clustering technique for detection of groups of outliers in online media and sequence data. The proposed models have been shown to be capable of explaining the underlying properties of the detected outliers. This can facilitate domain experts in narrowing down the scope of analysis and understanding the reasons of such anomalous behaviors. We have also investigated the reproducibility of the proposed models in similar application domains.
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| 4. |
- Abghari, Shahrooz, et al.
(författare)
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Higher order mining for monitoring district heating substations
- 2019
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Ingår i: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728144931 ; , s. 382-391
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Konferensbidrag (refereegranskat)abstract
- We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques.
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| 5. |
- Abghari, Shahrooz, et al.
(författare)
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Outlier Detection for Video Session Data Using Sequential Pattern Mining
- 2018
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Ingår i: ACM SIGKDD Workshop On Outlier Detection De-constructed.
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Konferensbidrag (refereegranskat)abstract
- The growth of Internet video and over-the-top transmission techniqueshas enabled online video service providers to deliver highquality video content to viewers. To maintain and improve thequality of experience, video providers need to detect unexpectedissues that can highly affect the viewers’ experience. This requiresanalyzing massive amounts of video session data in order to findunexpected sequences of events. In this paper we combine sequentialpattern mining and clustering to discover such event sequences.The proposed approach applies sequential pattern mining to findfrequent patterns by considering contextual and collective outliers.In order to distinguish between the normal and abnormal behaviorof the system, we initially identify the most frequent patterns. Thena clustering algorithm is applied on the most frequent patterns.The generated clustering model together with Silhouette Index areused for further analysis of less frequent patterns and detectionof potential outliers. Our results show that the proposed approachcan detect outliers at the system level.
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| 6. |
- Abghari, Shahrooz, et al.
(författare)
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Trend analysis to automatically identify heat program changes
- 2017
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Ingår i: Energy Procedia. - : Elsevier. ; , s. 407-415
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Konferensbidrag (refereegranskat)abstract
- The aim of this study is to improve the monitoring and controlling of heating systems located at customer buildings through the use of a decision support system. To achieve this, the proposed system applies a two-step classifier to detect manual changes of the temperature of the heating system. We apply data from the Swedish company NODA, active in energy optimization and services for energy efficiency, to train and test the suggested system. The decision support system is evaluated through an experiment and the results are validated by experts at NODA. The results show that the decision support system can detect changes within three days after their occurrence and only by considering daily average measurements.
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| 7. |
- Acevedo, Carlos
(författare)
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Developing Inclusive Innovation Processes and Co-Evolutionary University-Society Approaches in Bolivia
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This study is part of a worldwide debate on inclusive innovation systems in developingcountries and particularly on the co-evolutionary processes taking place, seen from theperspective of a public university. The increasing literature that discusses how innovationsystems and development can foster more inclusive and sustainable societies hasinspired this thesis work. Thus, the main problem handled in the research concerns thequestion how socially sensitive research practices and policies at a public university inBolivia can be stimulated within emerging innovation system dynamics. In that vein,empirical knowledge is developed at the Universidad Mayor de San SimoÅLn (UMSS),Cochabamba as a contribution to experience-based learning in the field. Analysis arenourished by a dialogue with the work of prominent Latin American scholars andpractitioners around the idea of a developmental university and the democratizationof knowledge. The reader will be able to recognize a recursive transit between theoryand practice, where a number of relevant concepts are contextualized and connectedin order to enable keys of critical interpretation and paths of practices amplificationfor social inclusion purposes established. The study shows how, based on a previousexperience, new competences and capacities for the Technology Transfer Unit (UTT)at UMSS were produced, in this case transforming itself into a University InnovationCentre. Main lessons gained in that experience came from two pilot cluster development(food and leather sectors) and a multidisciplinary researchers network (UMSSInnovation Team) where insights found can improve future collaborative relations betweenuniversity and society for inclusive innovation processes within the Boliviancontext.
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| 8. |
- Acevedo Peña, Carlos Gonzalo
(författare)
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Developing Inclusive Innovation Processes and Co-Evolutionary Approaches in Bolivia
- 2015
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The concept of National Innovation Systems (NIS) has been widely adopted in developing countries, particularly in Latin American countries, for the last two decades. The concept is used mainly as an ex-ante framework to organize and increase the dynamics of those institutions linked to science, technology and innovation, for catching-up processes of development. In the particular case of Bolivia, and after several decades of social and economic crisis, the promise of a national innovation system reconciles a framework for collaboration between the university, the government and the socio-productive sectors. Dynamics of collaboration generated within NIS can be a useful tool for the pursuit of inclusive development ambitions. This thesis is focused on inclusive innovation processes and the generation of co-evolutionary processes between university, government and socio-productive sectors. This is the result of 8 years of participatory action research influenced by Mode 2 knowledge-production and Technoscientific approaches. The study explores the policy paths the Bolivian government has followed in the last three decades in order to organize science, technology and innovation. It reveals that Bolivia has an emerging national innovation system, where its demand-pulled innovation model presents an inclusive approach. Innovation policy efforts in Bolivia are led by the Vice-Ministry of Science and Technology (VCyT). Moreover, NIS involves relational and collaborative approaches between institutions, which imply structural and organizational challenges, particularly for public universities, as they concentrate most of the research capabilities in the country. These universities are challenged to participate in NIS within contexts of weak demanding sectors. This research focuses on the early empirical approaches and transformations at Universidad Mayor de San Simón (UMSS) in Cochabamba. The aim to strengthen internal innovation capabilities of the university and enhance the relevance of research activities in society by supporting socio-economic development in the framework of innovation systems is led by the Technology Transfer Unit (UTT) at UMSS. UTT has become a recognized innovation facilitator unit, inside and outside the university, by proposing pro-active initiatives to support emerging innovation systems. Because of its complexity, the study focuses particularly on cluster development promoted by UTT. Open clusters are based on linking mechanisms between the university research capabilities, the socio-productive actors and government. Cluster development has shown to be a practical mechanism for the university to meet the demanding sector (government and socio-productive actors) and to develop trust-based inclusive innovation processes. The experiences from cluster activities have inspired the development of new research policies at UMSS, with a strong orientation to foster research activities towards an increased focus on socio-economic development. The experiences gained at UMSS are discussed and presented as a “developmental university” approach. Inclusive innovation processes with co-evolutionary approaches seem to constitute an alternative path supporting achievement of inclusive development ambitions in Bolivia.
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| 9. |
- Acs, Zoltan J., et al.
(författare)
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Philippe Aghion : recipient of the 2016 Global Award for Entrepreneurship Research
- 2017
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Ingår i: Small Business Economics. - : Springer. - 0921-898X .- 1573-0913. ; 48:1, s. 1-8
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Tidskriftsartikel (refereegranskat)abstract
- Professor Philippe Aghion is the 2016 recipient of the Global Award for Entrepreneurship Research, consisting of 100,000 Euros and a statuette designed by the internationally renowned Swedish sculptor Carl Milles. He is one of the most influential researchers worldwide in economics in the last couple of decades. His research has advanced our understanding of the relationship between firm-level innovation, entry and exit on the one hand, and productivity and growth on the other. Aghion has thus accomplished to bridge theoretical macroeconomic growth models with a more complete and consistent microeconomic setting. He is one of the founding fathers of the pioneering and original contribution referred to as Schumpeterian growth theory. Philippe Aghion has not only contributed with more sophisticated theoretical models, but also provided empirical evidence regarding the importance of entrepreneurial endeavours for societal prosperity, thereby initiating a more nuanced policy discussion concerning the interdependencies between entrepreneurship, competition, wealth and growth.
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| 10. |
- Adamov, Alexander, et al.
(författare)
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A Sandboxing Method to Protect Cloud Cyberspace
- 2015
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Ingår i: PROCEEDINGS OF 2015 IEEE EAST-WEST DESIGN & TEST SYMPOSIUM (EWDTS). - : IEEE Communications Society. - 9781467377768
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of protecting cloud environments against targeted attacks, which have become a popular mean of gaining access to organization's confidential information and resources of cloud providers. Only in 2015 eleven targeted attacks have been discovered by Kaspersky Lab. One of them - Duqu2 - successfully attacked the Lab itself. In this context, security researchers show rising concern about protecting corporate networks and cloud infrastructure used by large organizations against such type of attacks. This article describes a possibility to apply a sandboxing method within a cloud environment to enforce security perimeter of the cloud.
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