Pub Date : 2013-07-10DOI: 10.1109/IISA.2013.6623728
F. Andritsos
Ports constitute crucial intermodal nodes in the freight and passenger transport network as well as important border control points. Their security is therefore of paramount importance not only because of their critical transport functions but also because of their specific role, as control points, in the regional, national and European security. Port security is a cornerstone for the implementation of the new international maritime transport security regime. The aim of the present paper is to analyse the problem, highlight the issues faced in a systematic way towards a better port security without penalising excessively the trade or the port related activities, with a particular emphasis on access control and identity management. Finally, two practical measures for increasing the EU port security are highlighted.
{"title":"Port security & access control: A systemic approach","authors":"F. Andritsos","doi":"10.1109/IISA.2013.6623728","DOIUrl":"https://doi.org/10.1109/IISA.2013.6623728","url":null,"abstract":"Ports constitute crucial intermodal nodes in the freight and passenger transport network as well as important border control points. Their security is therefore of paramount importance not only because of their critical transport functions but also because of their specific role, as control points, in the regional, national and European security. Port security is a cornerstone for the implementation of the new international maritime transport security regime. The aim of the present paper is to analyse the problem, highlight the issues faced in a systematic way towards a better port security without penalising excessively the trade or the port related activities, with a particular emphasis on access control and identity management. Finally, two practical measures for increasing the EU port security are highlighted.","PeriodicalId":261368,"journal":{"name":"IISA 2013","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126001756","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}
Pub Date : 2013-07-10DOI: 10.1109/IISA.2013.6623695
M. Alamaniotis, H. Hernandez, T. Jevremovic
Analysis of pulse height gamma-ray signals is crucial in a variety of applications regarding safeguards and homeland security. Because of the inherent random nature of radiation measurements, the spectra obtained from gamma-ray sources exhibit a high variance that can be modeled as Poisson fluctuation. This variance imposes serious difficulties to spectrum analysis and isotope identification algorithms. To that end, artificial intelligence offers a variety of tools for automated, accurate, and the fast processing of gamma-ray signals. This paper discusses the use of a support vector regression (SVR) based methodology for removing Poisson fluctuation from pulse height radiation spectra. The proposed methodology utilizes an interval based smoothing of the spectrum and subsequently suppresses the variance. Methodology performance is tested on gamma-ray spectra taken with a low-resolution sodium iodide detector having a length of 1024 bins. Furthermore, this SVR technique is benchmarked against the 3-point and 7-point simple moving average methods. The results of this benchmarking demonstrate the effectiveness of the proposed methodology in removing Poisson fluctuation over the other methods tested.
{"title":"Application of support vector regression in removing Poisson fluctuation from pulse height gamma-ray spectra","authors":"M. Alamaniotis, H. Hernandez, T. Jevremovic","doi":"10.1109/IISA.2013.6623695","DOIUrl":"https://doi.org/10.1109/IISA.2013.6623695","url":null,"abstract":"Analysis of pulse height gamma-ray signals is crucial in a variety of applications regarding safeguards and homeland security. Because of the inherent random nature of radiation measurements, the spectra obtained from gamma-ray sources exhibit a high variance that can be modeled as Poisson fluctuation. This variance imposes serious difficulties to spectrum analysis and isotope identification algorithms. To that end, artificial intelligence offers a variety of tools for automated, accurate, and the fast processing of gamma-ray signals. This paper discusses the use of a support vector regression (SVR) based methodology for removing Poisson fluctuation from pulse height radiation spectra. The proposed methodology utilizes an interval based smoothing of the spectrum and subsequently suppresses the variance. Methodology performance is tested on gamma-ray spectra taken with a low-resolution sodium iodide detector having a length of 1024 bins. Furthermore, this SVR technique is benchmarked against the 3-point and 7-point simple moving average methods. The results of this benchmarking demonstrate the effectiveness of the proposed methodology in removing Poisson fluctuation over the other methods tested.","PeriodicalId":261368,"journal":{"name":"IISA 2013","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124053698","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}
Pub Date : 2013-07-10DOI: 10.1109/IISA.2013.6623712
C. Troussas, M. Virvou, Jaime D. L. Caro, K. Espinosa
Facebook applications are more convenient and flexible to use, since students tend to invest a good deal of time in the use of such technologies, given that Facebook retains the educational quality, as it is widely used in instructional contexts. To this direction, our Facebook application promotes language learning. Furthermore, it incorporates machine learning techniques in order to offer user classification based on multiple user characteristics. Our resulting Facebook language learning application has been evaluated by both instructors and Facebook users. The results of the evaluation showed that Facebook provides opportunities within curriculum education and learning and user classification in such applications promotes the cognitive procedure.
{"title":"Evaluation of a language learning application in Facebook","authors":"C. Troussas, M. Virvou, Jaime D. L. Caro, K. Espinosa","doi":"10.1109/IISA.2013.6623712","DOIUrl":"https://doi.org/10.1109/IISA.2013.6623712","url":null,"abstract":"Facebook applications are more convenient and flexible to use, since students tend to invest a good deal of time in the use of such technologies, given that Facebook retains the educational quality, as it is widely used in instructional contexts. To this direction, our Facebook application promotes language learning. Furthermore, it incorporates machine learning techniques in order to offer user classification based on multiple user characteristics. Our resulting Facebook language learning application has been evaluated by both instructors and Facebook users. The results of the evaluation showed that Facebook provides opportunities within curriculum education and learning and user classification in such applications promotes the cognitive procedure.","PeriodicalId":261368,"journal":{"name":"IISA 2013","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129313934","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}
Pub Date : 2013-07-10DOI: 10.1109/IISA.2013.6623718
E. Darra, S. Katsikas
Wireless Sensor Networks (WSN) are large systems that consist of low-cost, and resource-constrained sensor nodes. These networks are susceptible to many kinds of attacks as they have limited memory, battery life and computational power. Intrusion Detection is a solution to secure WSNs against several kinds of attacks. In this paper, we review types of attacks against WSNs and relevant intrusion detection approaches so that the attack detection capabilities of the latter are identified.
{"title":"Attack detection capabilities of intrusion detection systems for Wireless Sensor Networks","authors":"E. Darra, S. Katsikas","doi":"10.1109/IISA.2013.6623718","DOIUrl":"https://doi.org/10.1109/IISA.2013.6623718","url":null,"abstract":"Wireless Sensor Networks (WSN) are large systems that consist of low-cost, and resource-constrained sensor nodes. These networks are susceptible to many kinds of attacks as they have limited memory, battery life and computational power. Intrusion Detection is a solution to secure WSNs against several kinds of attacks. In this paper, we review types of attacks against WSNs and relevant intrusion detection approaches so that the attack detection capabilities of the latter are identified.","PeriodicalId":261368,"journal":{"name":"IISA 2013","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123388267","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}
Pub Date : 2013-07-10DOI: 10.1109/IISA.2013.6623726
Thomas Chatzidimitris, E. Kavakli, M. Economou, D. Gavalas
The paper focuses on current practice regarding the application of mobile Augmented Reality (AR) technologies for enabling learning in the context of cultural heritage. It also presents ARmuseum, an application developed for the Museum of Industrial Olive Oil Production in Lesvos (MBEL). Finally, it discusses a number of issues related to the evaluation of mobile AR applications for cultural institutions.
{"title":"Mobile Augmented Reality edutainment applications for cultural institutions","authors":"Thomas Chatzidimitris, E. Kavakli, M. Economou, D. Gavalas","doi":"10.1109/IISA.2013.6623726","DOIUrl":"https://doi.org/10.1109/IISA.2013.6623726","url":null,"abstract":"The paper focuses on current practice regarding the application of mobile Augmented Reality (AR) technologies for enabling learning in the context of cultural heritage. It also presents ARmuseum, an application developed for the Museum of Industrial Olive Oil Production in Lesvos (MBEL). Finally, it discusses a number of issues related to the evaluation of mobile AR applications for cultural institutions.","PeriodicalId":261368,"journal":{"name":"IISA 2013","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134478412","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}
Pub Date : 2013-07-10DOI: 10.1109/IISA.2013.6623678
M. Poulos, S. Papavlasopoulos
A data mining of Time Series using Autocorrelation Coefficients (ACC) and LVQ -Neural Network is addressed in this work-a problem that has not yet been seen in a signal processing framework, to the best of our knowledge. Neural network classification was performed on real Time series Data of real data, in an attempt to experimentally investigate the connection between Time Series data and hidden information about the properties of stationary Time Series. Finally, the ability of the ACC will be tested via a well fitted LVQ neural network which gives satisfactory results in predicting Time Series.
{"title":"Automatic stationary detection of time series using auto-correlation coefficients and LVQ — Neural network","authors":"M. Poulos, S. Papavlasopoulos","doi":"10.1109/IISA.2013.6623678","DOIUrl":"https://doi.org/10.1109/IISA.2013.6623678","url":null,"abstract":"A data mining of Time Series using Autocorrelation Coefficients (ACC) and LVQ -Neural Network is addressed in this work-a problem that has not yet been seen in a signal processing framework, to the best of our knowledge. Neural network classification was performed on real Time series Data of real data, in an attempt to experimentally investigate the connection between Time Series data and hidden information about the properties of stationary Time Series. Finally, the ability of the ACC will be tested via a well fitted LVQ neural network which gives satisfactory results in predicting Time Series.","PeriodicalId":261368,"journal":{"name":"IISA 2013","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133116984","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}
Pub Date : 2013-07-10DOI: 10.1109/IISA.2013.6623699
E. Ciancamerla, M. Minichino, S. Palmieri
Critical infrastructures, such as electrical grids, are monitored and controlled by SCADA (Supervisory Control And Data Acquisition) systems. Cyber attacks against SCADA might put CI and in turn industrial production, environment integrity and human safety at risk. Here, with reference to an actual case study, constituted by an electrical grid, its SCADA system and a corporate network, we discuss how cyber threats, vulnerabilities and attacks might degrade the functionalities of SCADA and corporate network and, in turn, lead to outages of the electrical grid. We represent SCADA and corporate network under malware propagation, Denial of Service and Man In The Middle attacks, and predict their consequent functionalities. Particularly, we use Netlogo to identify possible malware propagation in relation to SCADA & corporate security policies adopted from the utility and NS2 simulator to compute the consequences of such cyber attacks on SCADA and in turn on electrical grid functionalities.
{"title":"Modeling cyber attacks on a critical infrastructure scenario","authors":"E. Ciancamerla, M. Minichino, S. Palmieri","doi":"10.1109/IISA.2013.6623699","DOIUrl":"https://doi.org/10.1109/IISA.2013.6623699","url":null,"abstract":"Critical infrastructures, such as electrical grids, are monitored and controlled by SCADA (Supervisory Control And Data Acquisition) systems. Cyber attacks against SCADA might put CI and in turn industrial production, environment integrity and human safety at risk. Here, with reference to an actual case study, constituted by an electrical grid, its SCADA system and a corporate network, we discuss how cyber threats, vulnerabilities and attacks might degrade the functionalities of SCADA and corporate network and, in turn, lead to outages of the electrical grid. We represent SCADA and corporate network under malware propagation, Denial of Service and Man In The Middle attacks, and predict their consequent functionalities. Particularly, we use Netlogo to identify possible malware propagation in relation to SCADA & corporate security policies adopted from the utility and NS2 simulator to compute the consequences of such cyber attacks on SCADA and in turn on electrical grid functionalities.","PeriodicalId":261368,"journal":{"name":"IISA 2013","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132396838","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}
Pub Date : 2013-07-10DOI: 10.1109/IISA.2013.6623719
Angeliki Mikeli, Dimitris Sotiros, Dimitris Apostolou, D. Despotis
Many websites provide visitors with the possibility to evaluate each item on more than one criteria. A commonly used rating scale is the one to five-star rating system or similar linguistic scales. Such scales are ordinal but the symbolic or lexical semantics convey information about the strength of user references in addition to the order of rated items. We refer to such scales as discrete ordered scales. We present AHP-Rec a method that treats user ratings as interval scale data and uses a multi-criteria approach for deriving predictions for user ratings. We use the data provided by Yahoo! Movies to demonstrate and evaluate the AHP-Rec recommender method. AHP-Rec takes as input the ratings each user gives to movies, calculates weights for each scale item that are personal for each user and provides its recommendation by aggregating preferences of similar users. Our method provides improved results over the state of the art single criterion method SVD++ and the multi-criteria method UTARec.
{"title":"A multi-criteria recommender system incorporating intensity of preferences","authors":"Angeliki Mikeli, Dimitris Sotiros, Dimitris Apostolou, D. Despotis","doi":"10.1109/IISA.2013.6623719","DOIUrl":"https://doi.org/10.1109/IISA.2013.6623719","url":null,"abstract":"Many websites provide visitors with the possibility to evaluate each item on more than one criteria. A commonly used rating scale is the one to five-star rating system or similar linguistic scales. Such scales are ordinal but the symbolic or lexical semantics convey information about the strength of user references in addition to the order of rated items. We refer to such scales as discrete ordered scales. We present AHP-Rec a method that treats user ratings as interval scale data and uses a multi-criteria approach for deriving predictions for user ratings. We use the data provided by Yahoo! Movies to demonstrate and evaluate the AHP-Rec recommender method. AHP-Rec takes as input the ratings each user gives to movies, calculates weights for each scale item that are personal for each user and provides its recommendation by aggregating preferences of similar users. Our method provides improved results over the state of the art single criterion method SVD++ and the multi-criteria method UTARec.","PeriodicalId":261368,"journal":{"name":"IISA 2013","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128842846","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}
Pub Date : 2013-07-10DOI: 10.1109/IISA.2013.6623708
A. Drosou, A. Tsimpiris, D. Kugiumtzis, Nikos Porfyriou, D. Ioannidis, D. Tzovaras
This paper presents a novel approach for improving the accuracy of existing 3D face recognition algorithms via the dimensionality reduction of the feature space. In particular, two feature selection methods based on information criteria are selected and benchmarked herein (i.e. the minimum Redundancy - Maximum Relevance (mRMR) and the Conditional Mutual Information with Nearest Neighbors estimate (CMINN)), on top of the geometric features provided by a state-of-the-art 3D face recognition algorithm. Experimental validation on a proprietary dataset of 53 subjects illustrates significant advances in performance of the proposed method when compared to the reference 3D face recognition system. The repeated computations on several non-overlapping, randomly selected, training and test sets from the ensemble of frames, give evidence for successful classification of the subjects based on a significantly reduced subset of features with smaller cardinality, as obtained by CMINN. Finally, the high recognition capacity of this small fraction of biometric features is validated by the convergence of both methods to the same level of classification accuracy as the size of the utilized feature subset increases.
{"title":"Dimensionality reduction for enhanced 3D face recognition","authors":"A. Drosou, A. Tsimpiris, D. Kugiumtzis, Nikos Porfyriou, D. Ioannidis, D. Tzovaras","doi":"10.1109/IISA.2013.6623708","DOIUrl":"https://doi.org/10.1109/IISA.2013.6623708","url":null,"abstract":"This paper presents a novel approach for improving the accuracy of existing 3D face recognition algorithms via the dimensionality reduction of the feature space. In particular, two feature selection methods based on information criteria are selected and benchmarked herein (i.e. the minimum Redundancy - Maximum Relevance (mRMR) and the Conditional Mutual Information with Nearest Neighbors estimate (CMINN)), on top of the geometric features provided by a state-of-the-art 3D face recognition algorithm. Experimental validation on a proprietary dataset of 53 subjects illustrates significant advances in performance of the proposed method when compared to the reference 3D face recognition system. The repeated computations on several non-overlapping, randomly selected, training and test sets from the ensemble of frames, give evidence for successful classification of the subjects based on a significantly reduced subset of features with smaller cardinality, as obtained by CMINN. Finally, the high recognition capacity of this small fraction of biometric features is validated by the convergence of both methods to the same level of classification accuracy as the size of the utilized feature subset increases.","PeriodicalId":261368,"journal":{"name":"IISA 2013","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128863845","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}
Pub Date : 2013-07-10DOI: 10.1109/IISA.2013.6623713
C. Troussas, M. Virvou, K. Espinosa, Kevin Llaguno, Jaime D. L. Caro
The growing expansion of contents, placed on the Web, provides a huge collection of textual resources. People share their experiences, opinions or simply talk just about whatever concerns them online. The large amount of available data attracts system developers, studying on automatic mining and analysis. In this paper, the primary and underlying idea is that the fact of knowing how people feel about certain topics can be considered as a classification task. People's feelings can be positive, negative or neutral. A sentiment is often represented in subtle or complex ways in a text. An online user can use a diverse range of other techniques to express his or her emotions. Apart from that, s/he may mix objective and subjective information about a certain topic. On top of that, data gathered from the World Wide Web often contain a lot of noise. Indeed, the task of automatic sentiment recognition in online text becomes more difficult for all the aforementioned reasons. Hence, we present how sentiment analysis can assist language learning, by stimulating the educational process and experimental results on the Naive Bayes Classifier.
{"title":"Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning","authors":"C. Troussas, M. Virvou, K. Espinosa, Kevin Llaguno, Jaime D. L. Caro","doi":"10.1109/IISA.2013.6623713","DOIUrl":"https://doi.org/10.1109/IISA.2013.6623713","url":null,"abstract":"The growing expansion of contents, placed on the Web, provides a huge collection of textual resources. People share their experiences, opinions or simply talk just about whatever concerns them online. The large amount of available data attracts system developers, studying on automatic mining and analysis. In this paper, the primary and underlying idea is that the fact of knowing how people feel about certain topics can be considered as a classification task. People's feelings can be positive, negative or neutral. A sentiment is often represented in subtle or complex ways in a text. An online user can use a diverse range of other techniques to express his or her emotions. Apart from that, s/he may mix objective and subjective information about a certain topic. On top of that, data gathered from the World Wide Web often contain a lot of noise. Indeed, the task of automatic sentiment recognition in online text becomes more difficult for all the aforementioned reasons. Hence, we present how sentiment analysis can assist language learning, by stimulating the educational process and experimental results on the Naive Bayes Classifier.","PeriodicalId":261368,"journal":{"name":"IISA 2013","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129260128","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}
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