M. Babenko, N. Chervyakov, Andrei Tchernykh, N. Kucherov, M. N. Shabalina, I. Vashchenko, G. Radchenko, Daniil Murga
This paper addresses error correction codes approach in order to improve the performance of BOINC under uncertainty of users' behavior. Redundant Residue Number System (RRNS) moduli set of the special form provides correction of user unfairness, reliability, decreased redundancy and load of the computing network. Error correction code in RRNS is improved by using error syndrome. It decreases the amount of computations required for data decoding up to 20/7 times compared to the projection methods. The proposed modification of the error syndrome allow to omit the assumption that clients are honest and reliable. The proposed approach decreases the execution time of the client programs asymptotically by 4 times. On the other hand, encoding with RRNS places some restrictions on the range of performed operations.
{"title":"Unfairness Correction in P2P Grids Based on Residue Number System of a Special Form","authors":"M. Babenko, N. Chervyakov, Andrei Tchernykh, N. Kucherov, M. N. Shabalina, I. Vashchenko, G. Radchenko, Daniil Murga","doi":"10.1109/DEXA.2017.46","DOIUrl":"https://doi.org/10.1109/DEXA.2017.46","url":null,"abstract":"This paper addresses error correction codes approach in order to improve the performance of BOINC under uncertainty of users' behavior. Redundant Residue Number System (RRNS) moduli set of the special form provides correction of user unfairness, reliability, decreased redundancy and load of the computing network. Error correction code in RRNS is improved by using error syndrome. It decreases the amount of computations required for data decoding up to 20/7 times compared to the projection methods. The proposed modification of the error syndrome allow to omit the assumption that clients are honest and reliable. The proposed approach decreases the execution time of the client programs asymptotically by 4 times. On the other hand, encoding with RRNS places some restrictions on the range of performed operations.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122879942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Computing machine learning models in the cloud remains a central problem in big data analytics. In this work, we introduce a cloud analytic system exploiting a parallel array DBMS based on a classical shared-nothing architecture. Our approach combines in-DBMS data summarization with mathematical processing in an external program. We study how to summarize a data set in parallel assuming a large number of processing nodes and how to further accelerate it with GPUs. In contrast to most big data analytic systems, we do not use Java, HDFS, MapReduce or Spark: our system is programmed in C++ and C on top of a traditional Unix le system. In our system, models are ef ciently computed using a suite of innovative parallel matrix operators, which compute comprehensive statistical summaries of a large input data set (matrix) in one pass, leaving the remaining mathematically complex computations, with matrices that t in RAM, to R. In order to be competitive with the Hadoop ecosystem (i.e. HDFS and Spark RDDs) we also introduce a parallel load operator for large matrices and an automated, yet exible, cluster con guration in the cloud. Experiments compare our system with Spark, showing orders of magnitude time improvement. A GPU with many cores widens the gap further. In summary, our system is a competitive solution.
{"title":"A Cloud System for Machine Learning Exploiting a Parallel Array DBMS","authors":"Yiqun Zhang, C. Ordonez, S. Johnsson","doi":"10.1109/DEXA.2017.21","DOIUrl":"https://doi.org/10.1109/DEXA.2017.21","url":null,"abstract":"Computing machine learning models in the cloud remains a central problem in big data analytics. In this work, we introduce a cloud analytic system exploiting a parallel array DBMS based on a classical shared-nothing architecture. Our approach combines in-DBMS data summarization with mathematical processing in an external program. We study how to summarize a data set in parallel assuming a large number of processing nodes and how to further accelerate it with GPUs. In contrast to most big data analytic systems, we do not use Java, HDFS, MapReduce or Spark: our system is programmed in C++ and C on top of a traditional Unix le system. In our system, models are ef ciently computed using a suite of innovative parallel matrix operators, which compute comprehensive statistical summaries of a large input data set (matrix) in one pass, leaving the remaining mathematically complex computations, with matrices that t in RAM, to R. In order to be competitive with the Hadoop ecosystem (i.e. HDFS and Spark RDDs) we also introduce a parallel load operator for large matrices and an automated, yet exible, cluster con guration in the cloud. Experiments compare our system with Spark, showing orders of magnitude time improvement. A GPU with many cores widens the gap further. In summary, our system is a competitive solution.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126618320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fatemeh Salehi Rizi, M. Granitzer, Konstantin Ziegler
In an ego-network, an individual (ego) organizes its friends (alters) in different groups (social circles). This social network can be efficiently analyzed after learning representations of the ego and its alters in a low-dimensional, real vector space. These representations are then easily exploited via statistical models for tasks such as social circle detection and prediction. Recent advances in language modeling via deep learning have inspired new methods for learning network representations. These methods can capture the global structure of networks. In this paper, we evolve these techniques to also encode the local structure of neighborhoods. Therefore, our local representations capture network features that are hidden in the global representation of large networks. We show that the task of social circle prediction benefits from a combination of global and local features generated by our technique.
{"title":"Global and Local Feature Learning for Ego-Network Analysis","authors":"Fatemeh Salehi Rizi, M. Granitzer, Konstantin Ziegler","doi":"10.1109/DEXA.2017.36","DOIUrl":"https://doi.org/10.1109/DEXA.2017.36","url":null,"abstract":"In an ego-network, an individual (ego) organizes its friends (alters) in different groups (social circles). This social network can be efficiently analyzed after learning representations of the ego and its alters in a low-dimensional, real vector space. These representations are then easily exploited via statistical models for tasks such as social circle detection and prediction. Recent advances in language modeling via deep learning have inspired new methods for learning network representations. These methods can capture the global structure of networks. In this paper, we evolve these techniques to also encode the local structure of neighborhoods. Therefore, our local representations capture network features that are hidden in the global representation of large networks. We show that the task of social circle prediction benefits from a combination of global and local features generated by our technique.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115350500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eissa Alshari, A. Azman, S. Doraisamy, N. Mustapha, Mustafa Alkeshr
Recently, many researchers have shown interest in using Word2Vec as the features for text classification tasks such as sentiment analysis. Its ability to model high quality distributional semantics among words has contributed to its success in many of the tasks. However, due to the high dimensional nature of the Word2Vec features, it increases the complexity for the classifier. In this paper, a method to construct a feature set based on Word2Vec is proposed for sentiment analysis. The method is based on clustering of terms in the vocabulary based on a set of opinion words from a sentiment lexical dictionary. As a result, the feature set for the classification is constructed based on the set of clusters. The effectiveness of the proposed method is evaluated on the Internet Movie Review Dataset with two classifiers, namely the Support Vector Machine and the Logistic Regression. The result is promising, showing that the proposed method can be more effective than the baseline approaches.
{"title":"Improvement of Sentiment Analysis Based on Clustering of Word2Vec Features","authors":"Eissa Alshari, A. Azman, S. Doraisamy, N. Mustapha, Mustafa Alkeshr","doi":"10.1109/DEXA.2017.41","DOIUrl":"https://doi.org/10.1109/DEXA.2017.41","url":null,"abstract":"Recently, many researchers have shown interest in using Word2Vec as the features for text classification tasks such as sentiment analysis. Its ability to model high quality distributional semantics among words has contributed to its success in many of the tasks. However, due to the high dimensional nature of the Word2Vec features, it increases the complexity for the classifier. In this paper, a method to construct a feature set based on Word2Vec is proposed for sentiment analysis. The method is based on clustering of terms in the vocabulary based on a set of opinion words from a sentiment lexical dictionary. As a result, the feature set for the classification is constructed based on the set of clusters. The effectiveness of the proposed method is evaluated on the Internet Movie Review Dataset with two classifiers, namely the Support Vector Machine and the Logistic Regression. The result is promising, showing that the proposed method can be more effective than the baseline approaches.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127770395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Tchernykh, M. Babenko, N. Chervyakov, J. M. Cortés-Mendoza, N. Kucherov, V. Miranda-López, M. Deryabin, I. Dvoryaninova, G. Radchenko
Cloud computing has become a part of people's lives. However, there are many unresolved problems with security of this technology. According to the assessment of international experts in the field of security, there are risks in the appearance of cloud collusion in uncertain conditions. To mitigate this type of uncertainty, and minimize data redundancy of encryption together with harms caused by cloud collusion, modified threshold Asmuth-Bloom and weighted Mignotte secret sharing schemes are used. We show that if the villains do know the secret parts, and/or do not know the secret key, they cannot recuperate the secret. If the attackers do not know the required number of secret parts but know the secret key, the probability that they obtain the secret depends the size of the machine word in bits that is less than 1/2 ((l-1)). We demonstrate that the proposed scheme ensures security under several types of attacks. We propose four approaches to select weights for secret sharing schemes to optimize the system behavior based on data access speed: pessimistic, balanced, and optimistic, and on speed per price ratio. We use the approximate method to improve the detection, localization and error correction accuracy under cloud parameters uncertainty.
{"title":"Towards Mitigating Uncertainty of Data Security Breaches and Collusion in Cloud Computing","authors":"A. Tchernykh, M. Babenko, N. Chervyakov, J. M. Cortés-Mendoza, N. Kucherov, V. Miranda-López, M. Deryabin, I. Dvoryaninova, G. Radchenko","doi":"10.1109/DEXA.2017.44","DOIUrl":"https://doi.org/10.1109/DEXA.2017.44","url":null,"abstract":"Cloud computing has become a part of people's lives. However, there are many unresolved problems with security of this technology. According to the assessment of international experts in the field of security, there are risks in the appearance of cloud collusion in uncertain conditions. To mitigate this type of uncertainty, and minimize data redundancy of encryption together with harms caused by cloud collusion, modified threshold Asmuth-Bloom and weighted Mignotte secret sharing schemes are used. We show that if the villains do know the secret parts, and/or do not know the secret key, they cannot recuperate the secret. If the attackers do not know the required number of secret parts but know the secret key, the probability that they obtain the secret depends the size of the machine word in bits that is less than 1/2 ((l-1)). We demonstrate that the proposed scheme ensures security under several types of attacks. We propose four approaches to select weights for secret sharing schemes to optimize the system behavior based on data access speed: pessimistic, balanced, and optimistic, and on speed per price ratio. We use the approximate method to improve the detection, localization and error correction accuracy under cloud parameters uncertainty.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132165937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cloud service recommendation has become an important technique that helps users decide whether a service satisfies their requirements or not. However, the few existing recommendation systems are not suitable for real world environments and only deal with services hosted in a single cloud, which is simply unrealistic. In addition, a same service may be hosted on more than one cloud and, hence, may have different user ratings that depend on specific conditions of their cloud availability zones. This uncertainty regarding the real quality of the cloud service and users' satisfaction levels raises a question about how to trust the different users' ratings in order to recommend the adequate cloud service. Unlike existing solutions, the goal of this work is to propose a cooperative recommender system that aims to resolve two major issues: recommendation of cloud services in multiple clouds and recommendation under uncertainty of users' ratings. The proposed system will take advantage from a set of powerful techniques and paradigms in order to offer an overlay of cloud recommender entities that cooperate to deliver top-rated services to the user.
{"title":"A Multi-Recommenders System for Service Provisioning in Multi-Cloud Environment","authors":"Haithem Mezni","doi":"10.1109/DEXA.2017.45","DOIUrl":"https://doi.org/10.1109/DEXA.2017.45","url":null,"abstract":"Cloud service recommendation has become an important technique that helps users decide whether a service satisfies their requirements or not. However, the few existing recommendation systems are not suitable for real world environments and only deal with services hosted in a single cloud, which is simply unrealistic. In addition, a same service may be hosted on more than one cloud and, hence, may have different user ratings that depend on specific conditions of their cloud availability zones. This uncertainty regarding the real quality of the cloud service and users' satisfaction levels raises a question about how to trust the different users' ratings in order to recommend the adequate cloud service. Unlike existing solutions, the goal of this work is to propose a cooperative recommender system that aims to resolve two major issues: recommendation of cloud services in multiple clouds and recommendation under uncertainty of users' ratings. The proposed system will take advantage from a set of powerful techniques and paradigms in order to offer an overlay of cloud recommender entities that cooperate to deliver top-rated services to the user.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127993137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The energy consumption of data centers has been increasing continuously during the last years due to the rising demands of computational power especially in current Grid- and Cloud Computing systems, which directly influence the increment in operational costs as well as carbon dioxide (CO2) emission. To reduce energy consumption within the cloud data center, it required energy-aware virtual machines (VMs) selection algorithms for VM consolidation at time host detected underloaded and overloaded and after allocating resources to all VMs from the underloaded hosts required to turn into energy saving-mode. In this paper, we propose energy-aware dynamic VM selection algorithms for consolidating the VMs from overloaded or underloaded host for minimising the total energy consumption and maximise the Quality of Service (QoS) include the reduction of service level agreements (SLAs) violation. To validate our scheme, we implemented it using CloudSim simulator and conducted simulations on the 10 different day's real workloads trace, which provided by the PlanetLab.
{"title":"MuMs: Energy-Aware VM Selection Scheme for Cloud Data Center","authors":"Rahul Yadav, Weizhe Zhang, Huangning Chen, T. Guo","doi":"10.1109/DEXA.2017.43","DOIUrl":"https://doi.org/10.1109/DEXA.2017.43","url":null,"abstract":"The energy consumption of data centers has been increasing continuously during the last years due to the rising demands of computational power especially in current Grid- and Cloud Computing systems, which directly influence the increment in operational costs as well as carbon dioxide (CO2) emission. To reduce energy consumption within the cloud data center, it required energy-aware virtual machines (VMs) selection algorithms for VM consolidation at time host detected underloaded and overloaded and after allocating resources to all VMs from the underloaded hosts required to turn into energy saving-mode. In this paper, we propose energy-aware dynamic VM selection algorithms for consolidating the VMs from overloaded or underloaded host for minimising the total energy consumption and maximise the Quality of Service (QoS) include the reduction of service level agreements (SLAs) violation. To validate our scheme, we implemented it using CloudSim simulator and conducted simulations on the 10 different day's real workloads trace, which provided by the PlanetLab.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115164490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roman Zoun, K. Schallert, David Broneske, R. Heyer, D. Benndorf, G. Saake
Metaproteomics is an analytic approach to research microorganisms that live in complex microbial communities. A key aspect of understanding microbial communities is to link the functions of proteins identified by metaproteomics to their taxonomy. In this paper we demonstrate the interactive chord visualization as a powerful tool to explore such data. To evaluate the tools efficacy, we use the relation data between functions and taxonomies from a large metaproteomics experiment. We evaluated the work flow in comparison to previous methods of data analysis and showed that interactive exploration of data using the chord diagram is significantly faster in four of five tasks. Therefore, the chord visualization improves the user's ability to discover complex biological relationships.
{"title":"Interactive Chord Visualization for Metaproteomics","authors":"Roman Zoun, K. Schallert, David Broneske, R. Heyer, D. Benndorf, G. Saake","doi":"10.1109/DEXA.2017.32","DOIUrl":"https://doi.org/10.1109/DEXA.2017.32","url":null,"abstract":"Metaproteomics is an analytic approach to research microorganisms that live in complex microbial communities. A key aspect of understanding microbial communities is to link the functions of proteins identified by metaproteomics to their taxonomy. In this paper we demonstrate the interactive chord visualization as a powerful tool to explore such data. To evaluate the tools efficacy, we use the relation data between functions and taxonomies from a large metaproteomics experiment. We evaluated the work flow in comparison to previous methods of data analysis and showed that interactive exploration of data using the chord diagram is significantly faster in four of five tasks. Therefore, the chord visualization improves the user's ability to discover complex biological relationships.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127221714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The combination of different knowledge bases in the field of information retrieval is called federated or aggregated search. It has several benefits over single source retrieval but poses some challenges as well. This work focuses on the challenge of result aggregation; especially in a setting where the final result list should include some level of diversity and serendipity. Both concepts have been shown to have an impact on how user perceive an information retrieval system. In particular, we want to assess if conventional procedures for result list aggregation can be utilised to introduce diversity and serendipity. Furthermore, we study whether blocking or interleaving for result aggregation yields better results. In a cross vertical aggregated search the so-called verticals could be news, multimedia content or text. Block ranking is one approach to combine such heterogeneous result. It relies on the idea that these verticals are combined into a single result list as blocks of several adjacent items. An alternative approach for this is interleaving. Here the verticals are blended into one result list on an item by item basis, i.e. adjacent items in the result list may come from different verticals. To generate the diverse and serendipitous results we relied on a query reformulation technique which we showed to be beneficial to produce diversified results in previous work. To conduct this evaluation we created a dedicated dataset. This dataset served as a basis for three different evaluation settings on a crowdsourcing platform, with over 300 participants. Our results show that query based diversification can be adapted to generate serendipitous results in a similar manner. Further, we discovered that both methods, interleaving and block ranking, appear to be beneficial to introduce diversity and serendipity. Though it seems that queries either benefit from one approach, or the other one, but not from both.
{"title":"Evaluation of Contextualization and Diversification Approaches in Aggregated Search","authors":"Hermann Ziak, Roman Kern","doi":"10.1109/DEXA.2017.37","DOIUrl":"https://doi.org/10.1109/DEXA.2017.37","url":null,"abstract":"The combination of different knowledge bases in the field of information retrieval is called federated or aggregated search. It has several benefits over single source retrieval but poses some challenges as well. This work focuses on the challenge of result aggregation; especially in a setting where the final result list should include some level of diversity and serendipity. Both concepts have been shown to have an impact on how user perceive an information retrieval system. In particular, we want to assess if conventional procedures for result list aggregation can be utilised to introduce diversity and serendipity. Furthermore, we study whether blocking or interleaving for result aggregation yields better results. In a cross vertical aggregated search the so-called verticals could be news, multimedia content or text. Block ranking is one approach to combine such heterogeneous result. It relies on the idea that these verticals are combined into a single result list as blocks of several adjacent items. An alternative approach for this is interleaving. Here the verticals are blended into one result list on an item by item basis, i.e. adjacent items in the result list may come from different verticals. To generate the diverse and serendipitous results we relied on a query reformulation technique which we showed to be beneficial to produce diversified results in previous work. To conduct this evaluation we created a dedicated dataset. This dataset served as a basis for three different evaluation settings on a crowdsourcing platform, with over 300 participants. Our results show that query based diversification can be adapted to generate serendipitous results in a similar manner. Further, we discovered that both methods, interleaving and block ranking, appear to be beneficial to introduce diversity and serendipity. Though it seems that queries either benefit from one approach, or the other one, but not from both.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115795907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Irene Gilpérez-López, J. Torregrosa, M. Barhamgi, David Camacho
Radicalization has increasingly become a transnational risk as cyber-technologies and social networks have steadily improved in the past years. DAESH has utilized the benefits of these new technologies to radicalize and recruit home-grown fighters, inviting them to join their ranks in the Islamic State territories, or inciting them to attack in their Western countries. However, this process of radicalization is not a simple task. Jihadist recruiters take advantage of certain vulnerable individuals who are better targets for radicalization. Academics have subsequently attempted to identify and examine these characteristics, which can be useful in identifying people who may be vulnerable to jihadist rhetoric. Violence risk assessment is a method which has been used by psychologists, Law Enforcement Agencies, prosecutors and other relevant actors, in order to assess the risk of an individual to commit violent acts. As terrorist or political violence is not comparable with other types of violence, specialized tools are needed to calculate the risk of radicalization or attack threat. This paper tries to gather the risk factors associated with violent extremism and jihadist radicalization from the literature and the risk assessment tools already available or under development. This paper presents some details related to the RiskTrack software tool, which is currently under development by behavioral researchers and computer engineers. The RiskTrack software tool aims to automatically assess an individual's risk of becoming radicalized. This is done by analyzing social media profiles and testing for specific factors that have been found to be associated with radicalization.
{"title":"An Initial Study on Radicalization Risk Factors: Towards an Assessment Software Tool","authors":"Irene Gilpérez-López, J. Torregrosa, M. Barhamgi, David Camacho","doi":"10.1109/DEXA.2017.19","DOIUrl":"https://doi.org/10.1109/DEXA.2017.19","url":null,"abstract":"Radicalization has increasingly become a transnational risk as cyber-technologies and social networks have steadily improved in the past years. DAESH has utilized the benefits of these new technologies to radicalize and recruit home-grown fighters, inviting them to join their ranks in the Islamic State territories, or inciting them to attack in their Western countries. However, this process of radicalization is not a simple task. Jihadist recruiters take advantage of certain vulnerable individuals who are better targets for radicalization. Academics have subsequently attempted to identify and examine these characteristics, which can be useful in identifying people who may be vulnerable to jihadist rhetoric. Violence risk assessment is a method which has been used by psychologists, Law Enforcement Agencies, prosecutors and other relevant actors, in order to assess the risk of an individual to commit violent acts. As terrorist or political violence is not comparable with other types of violence, specialized tools are needed to calculate the risk of radicalization or attack threat. This paper tries to gather the risk factors associated with violent extremism and jihadist radicalization from the literature and the risk assessment tools already available or under development. This paper presents some details related to the RiskTrack software tool, which is currently under development by behavioral researchers and computer engineers. The RiskTrack software tool aims to automatically assess an individual's risk of becoming radicalized. This is done by analyzing social media profiles and testing for specific factors that have been found to be associated with radicalization.","PeriodicalId":127009,"journal":{"name":"2017 28th International Workshop on Database and Expert Systems Applications (DEXA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127898428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：