The publication of fake reviews by parties with vested interests has become a severe problem for ... more The publication of fake reviews by parties with vested interests has become a severe problem for consumers who use online product reviews in their decision making. To counter this problem a number of methods for detecting these fake reviews, termed opinion spam, have been proposed. However, to date, many of these methods focus on analysis of review text, making them unsuitable for many review systems where accompanying text is optional, or not possible. Moreover, these approaches are often computationally expensive, requiring extensive resources to handle text analysis over the scale of data typically involved. In this paper, we consider opinion spammers manipulation of average ratings for products, focusing on differences between spammer ratings and the majority opinion of honest reviewers. We propose a lightweight, effective method for detecting opinion spammers based on these differences. This method uses binomial regression to identify reviewers having an anomalous proportion of ratings that deviate from the majority opinion. Experiments on real-world and synthetic data show that our approach is able to successfully identify opinion spammers. Comparison with the current state-of-the-art approach, also based only on ratings, shows that our method is able to achieve similar detection accuracy while removing the need for assumptions regarding probabilities of spam and non-spam reviews and reducing the heavy computation required for learning.
Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection... more Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, and as being labelled or unlabelled, and survey methods for detecting these di↵erent types of anomalies. We suggest that the detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space. In addition, this paper provides an overview of the types of problems that anomaly detection can address and identifies key areas for future research.
—Reputation-based trust models are widely used in e-commerce applications, and feedback ratings a... more —Reputation-based trust models are widely used in e-commerce applications, and feedback ratings are aggregated to compute sellers' reputation trust scores. The " all good reputation " problem, however, is prevalent in current reputation systems— reputation scores are universally high for sellers and it is difficult for potential buyers to select trustworthy sellers. In this paper, based on the observation that buyers often express opinions openly in free text feedback comments, we propose CommTrust for trust evaluation by mining feedback comments. Our main contributions include: 1) we propose a multidimensional trust model for computing reputation scores from user feedback comments; and 2) we propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining, and topic modeling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the " all good reputation " issue and rank sellers effectively. To the best of our knowledge, our research is the first piece of work on trust evaluation by mining feedback comments.
The Melbourne team was a collaboration between academic and industry groups. The team participate... more The Melbourne team was a collaboration between academic and industry groups. The team participated in both the learning and the interactive tasks of this year's Legal Track. The baseline run for the learning track employed true-relevance feedback, achieving respectable outcomes; the experimental runs added additional features and employed an SVM classifier, with poor results. The techniques developed for the learning task were then deployed in the interactive task. The classifier again achieved poor predictive quality, although final results place our production (non-significantly) first. We describe the learning task efforts in Section 2, and the interactive task in Section 3.
The rich non-factual information on the blogosphere presents interesting research questions. In t... more The rich non-factual information on the blogosphere presents interesting research questions. In this paper, we present a study on analysis of blog posts for their sentiment by using a generic sentiment lexicon. In particular, we applied Support Vector Machine to classify blog posts into three categories of opinions: positive, negative and mixed. We investigated the performance difference between global topic-independent and local topic-dependent opinion classification on a collection of blogs. Our experiment shows that topic-dependent classification performs significantly better than topic-independent classification, and this result indicates high interaction between sentiment words and topic.
This paper reports RMIT's participation in the TREC Blog Track 2010. For the baseline task, we ad... more This paper reports RMIT's participation in the TREC Blog Track 2010. For the baseline task, we adopted the BM25 model implemented in the Zettair search engine to establish a retrieval system of blog posts based on topic relevance. We then experimented with a number of different approaches to aggregate the post similarity scores to retrieve the most relevant blogs. Similarly, for the faceted distillation task we built a system at the post level first. After that, scores are aggregated for blogs to re-rank the most relevant blogs for the facet inclinations. A SVM classifier has been trained on Blog 06 collection to produce the opinion scores for each post. The cross entropy is used to evaluate posts for the in-depth versus shallow facet. For the personal versus official facet, we assumed blogs which are opinionated are also personal.
Efficiently Computing Iceberg Cubes with Complex Constraints through Bounding
Lecture Notes in Computer Science, 2003
Data cubes facilitate fast On-Line Analytical Processing (OLAP). Iceberg cubes are special cubes ... more Data cubes facilitate fast On-Line Analytical Processing (OLAP). Iceberg cubes are special cubes comprise only the multidimensional groups satisfying some user-specified constraints. Previous algorithms have focused on iceberg cubes defined by relatively simple constraints such as “COUNT(*) ≥ δ” and “COUNT(*) ≥ δ AND AV G(Profit) ≥ α”. We propose an algorithm I-Cubing that computes iceberg cubes defined by complex constraints involving multiple predicates of aggregates such as “COUNT(*) ≥ δ AND (AV G(Profit) ≥ α OR AV G(profit) ≤ β)”. State-of-the-art iceberg cubing algorithms: BUC cannot handle such cases whereas H-Cubing has to incur extra cost. Our proposed bounding technique can prune for all the given constraints at once without extra cost. Experiments show that bounding has superior pruning power and I-Cubing is twice as fast as H-Cubing. Furthermore, I-Cubing performs equally well with more complex constraints.
2011 IEEE International Conference on Web Services, 2011
The trust of sellers and transactions is a very important issue in e-commerce and e-service envir... more The trust of sellers and transactions is a very important issue in e-commerce and e-service environments. At some e-commerce websites (such as eBay 1 ), the trust management mechanism can compute a trust value of a seller, which is based on the ratings of past transactions given by buyers. This trust value, however, is static and can only reflect the general or global trust status of a seller, and it is not directly bound to a new transaction. As a result, a buyer may be easily cheated by a malicious seller in a new transaction with the notorious value imbalance problem [5], i.e., the malicious seller can build up a good reputation by selling cheap products/services and then start to cheat buyers by selling expensive products/services. Instead of providing such a static trust value, in order to provide more objective trust result for a new potential transaction, a trust evaluation mechanism should based on the ratings of past transactions, the nature of both past transactions and the new transaction. In this paper, we propose a new contextual trust evaluation method. Our method compares the transaction context similarity between the new transaction and past transactions, from which the trust value of the new transaction can be determined. Our method can identify and prevent potentially malicious transactions with the value imbalance problem.
The Star-cubing algorithm performs multiway aggregation on trees but incurs huge memory consumpti... more The Star-cubing algorithm performs multiway aggregation on trees but incurs huge memory consumption. We propose a new algorithm MG-cubing that achieves maximal multiway aggregation. Our experiments show that MG-cubing achieves similar and very often better time and memory efficiency than Star-cubing.
Inferring Aggregation Hierarchies for Integration of Data Marts
Lecture Notes in Computer Science, 2010
The problem of integrating heterogeneous data marts is an important problem in building enterpris... more The problem of integrating heterogeneous data marts is an important problem in building enterprise data warehouses. Specially identifying compatible dimensions is crucial to successful integration. Existing notions of dimension compatibility rely on given and exact dimension hierarchy information being available. In this paper, we propose to infer aggregation hierarchies for dimensions from a database instance and use these inferred aggregation
Association rules describe the degree of dependence between items in transactional datasets by th... more Association rules describe the degree of dependence between items in transactional datasets by their confidences. In this paper, we first introduce the problem of mining top rules, namely those association rules with 100% confidence. Traditional approaches to this problem need a minimum support (minsup) threshold and then can discover the top rules with supports minsup; such approaches, however, rely on minsup to help avoid examining too many candidates and they miss those top rules whose supports are below minsup. The low support top rules (e.g. some unusual combinations of some factors that have always caused some disease) may be very interesting. Fundamentally different from previous work, our proposed method uses a dataset partitioning technique and two border-based algorithms to efficiently discover all top rules with a given consequent, without the constraint of support threshold. Importantly, we use borders to concisely represent all top rules, instead of enumerating them individually. We also discuss how to discover all zero-confidence rules and some very high (say 90%) confidence rules using approaches similar to mining top rules. Experimental results using the Mushroom, the Cleveland heart disease, and the Boston housing datasets are reported to evaluate the efficiency of the proposed approach.
Predicting Disordered Regions in Proteins Based on Decision Trees of Reduced Amino Acid Composition
Journal of Computational Biology, 2006
Intrinsically unstructured proteins (IUPs) are proteins lacking a fixed three dimensional structu... more Intrinsically unstructured proteins (IUPs) are proteins lacking a fixed three dimensional structure or containing long disordered regions. IUPs play an important role in biology and disease. Identifying disordered regions in protein sequences can provide useful information on protein structure and function, and can assist high-throughput protein structure determination. In this paper we present a system for predicting disordered regions in proteins based on decision trees and reduced amino acid composition. Concise rules based on biochemical properties of amino acid side chains are generated for prediction. Coarser information extracted from the composition of amino acids can not only improve the prediction accuracy but also increase the learning efficiency. In cross-validation tests, with four groups of reduced amino acid composition, our system can achieve a recall of 80% at a 13% false positive rate for predicting disordered regions, and the overall accuracy can reach 83.4%. This prediction accuracy is comparable to most, and better than some, existing predictors. Advantages of our approach are high prediction accuracy for long disordered regions and efficiency for large-scale sequence analysis. Our software is freely available for academic use upon request.
In migrating a legacy relational database system to object-oriented(OO) platform, when database m... more In migrating a legacy relational database system to object-oriented(OO) platform, when database migration completes, application modules are to be migrated, where embedded relational database operations are mapped into their OO correspondents. In this paper we study mapping relational update operations to their OO equivalents, which include UPDATE * , INSERT and DELETE operations. Relational update operation translation from relational to OO faces the touchy problem of transformation from a value-based relationship model to a reference-based model and maintaining the relational integrity constraints. Moreover, with a relational database where inheritance is expressed as attribute value subset relationship, changing of some attribute values may lead to the change of the position of an object in the class inheritance hierarchy, which we call object migration.
IEEE Transactions on Knowledge and Data Engineering, 2000
The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy gi... more The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy given aggregation constraints. Pruning unproductive computation for iceberg cubing when nonantimonotone constraints are present is a great challenge because the aggregate functions do not increase or decrease monotonically along the subset relationship between partitions. In this paper, we propose a novel bound prune cubing (BP-Cubing) approach for iceberg cubing with nonantimonotone aggregation constraints. Given a cube over n dimensions, an aggregate for any group-by partition can be computed from aggregates for the most specific n-dimensional partitions (MSPs). The largest and smallest aggregate values computed this way become the bounds for all partitions in the cube. We provide efficient methods to compute tight bounds for base aggregate functions and, more interestingly, arithmetic expressions thereof, from bounds of aggregates over the MSPs. Our methods produce tighter bounds than those obtained by previous approaches. We present iceberg cubing algorithms that combine bounding with efficient aggregation strategies. Our experiments on real-world and artificial benchmark data sets demonstrate that BP-Cubing algorithms achieve more effective pruning and are several times faster than state-of-the-art iceberg cubing algorithms and that BP-Cubing achieves the best performance with the top-down cubing approach.
Determination of Diethylstilbestrol in Aquatic Products by High Performance Liquid Chromatography with Fluorescence Detection
Chinese Journal of Chemistry, 2010
ABSTRACT A sensitive method for the determination of diethylstilbestrol (DES) in aquatic products... more ABSTRACT A sensitive method for the determination of diethylstilbestrol (DES) in aquatic products by high performance liquid chromatography (HPLC) using a fluorescence detector (FLD) was developed. By means of sulfonation in concentrated sulfuric acid and hydrolysis, DES was converted into a reaction product that can emit fluorescence and determined by HPLC. For the first time, HPLC with FLD was applied to the determination of DES and sensitivity comparable to that of LC-MS gained. The limit of detection was 0.1 µg·kg−1, and the recoveries were above 86% with relative standard deviations less than 6.2%. The proposed method was successfully applied to the determination of DES in aquatic products.
Role-based access control (RBAC) has attracted considerable research interest. However, the compu... more Role-based access control (RBAC) has attracted considerable research interest. However, the computational issues of RBAC models are yet to be thoroughly studied. In this paper, we study the problem of efficient maintenance of large RBAC models in a database-based multi-domain Web service environment. We propose first-order (SQL) algorithms to maintain the reachability of access roles under dynamic changes. The main advantages of our algorithms are: the support of various operations required for managing access roles with fractional information of roles; the maintenance of an update through operating a bounded number of join operations despite of the data size. To the best of our knowledge, our algorithms are the first attempt to maintain RBAC models using a first-order language.
The publication of fake reviews by parties with vested interests has become a severe problem for ... more The publication of fake reviews by parties with vested interests has become a severe problem for consumers who use online product reviews in their decision making. To counter this problem a number of methods for detecting these fake reviews, termed opinion spam, have been proposed. However, to date, many of these methods focus on analysis of review text, making them unsuitable for many review systems where accompanying text is optional, or not possible. Moreover, these approaches are often computationally expensive, requiring extensive resources to handle text analysis over the scale of data typically involved. In this paper, we consider opinion spammers manipulation of average ratings for products, focusing on differences between spammer ratings and the majority opinion of honest reviewers. We propose a lightweight, effective method for detecting opinion spammers based on these differences. This method uses binomial regression to identify reviewers having an anomalous proportion of ratings that deviate from the majority opinion. Experiments on real-world and synthetic data show that our approach is able to successfully identify opinion spammers. Comparison with the current state-of-the-art approach, also based only on ratings, shows that our method is able to achieve similar detection accuracy while removing the need for assumptions regarding probabilities of spam and non-spam reviews and reducing the heavy computation required for learning.
Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection... more Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, and as being labelled or unlabelled, and survey methods for detecting these di↵erent types of anomalies. We suggest that the detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space. In addition, this paper provides an overview of the types of problems that anomaly detection can address and identifies key areas for future research.
—Reputation-based trust models are widely used in e-commerce applications, and feedback ratings a... more —Reputation-based trust models are widely used in e-commerce applications, and feedback ratings are aggregated to compute sellers' reputation trust scores. The " all good reputation " problem, however, is prevalent in current reputation systems— reputation scores are universally high for sellers and it is difficult for potential buyers to select trustworthy sellers. In this paper, based on the observation that buyers often express opinions openly in free text feedback comments, we propose CommTrust for trust evaluation by mining feedback comments. Our main contributions include: 1) we propose a multidimensional trust model for computing reputation scores from user feedback comments; and 2) we propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining, and topic modeling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the " all good reputation " issue and rank sellers effectively. To the best of our knowledge, our research is the first piece of work on trust evaluation by mining feedback comments.
The Melbourne team was a collaboration between academic and industry groups. The team participate... more The Melbourne team was a collaboration between academic and industry groups. The team participated in both the learning and the interactive tasks of this year's Legal Track. The baseline run for the learning track employed true-relevance feedback, achieving respectable outcomes; the experimental runs added additional features and employed an SVM classifier, with poor results. The techniques developed for the learning task were then deployed in the interactive task. The classifier again achieved poor predictive quality, although final results place our production (non-significantly) first. We describe the learning task efforts in Section 2, and the interactive task in Section 3.
The rich non-factual information on the blogosphere presents interesting research questions. In t... more The rich non-factual information on the blogosphere presents interesting research questions. In this paper, we present a study on analysis of blog posts for their sentiment by using a generic sentiment lexicon. In particular, we applied Support Vector Machine to classify blog posts into three categories of opinions: positive, negative and mixed. We investigated the performance difference between global topic-independent and local topic-dependent opinion classification on a collection of blogs. Our experiment shows that topic-dependent classification performs significantly better than topic-independent classification, and this result indicates high interaction between sentiment words and topic.
This paper reports RMIT's participation in the TREC Blog Track 2010. For the baseline task, we ad... more This paper reports RMIT's participation in the TREC Blog Track 2010. For the baseline task, we adopted the BM25 model implemented in the Zettair search engine to establish a retrieval system of blog posts based on topic relevance. We then experimented with a number of different approaches to aggregate the post similarity scores to retrieve the most relevant blogs. Similarly, for the faceted distillation task we built a system at the post level first. After that, scores are aggregated for blogs to re-rank the most relevant blogs for the facet inclinations. A SVM classifier has been trained on Blog 06 collection to produce the opinion scores for each post. The cross entropy is used to evaluate posts for the in-depth versus shallow facet. For the personal versus official facet, we assumed blogs which are opinionated are also personal.
Efficiently Computing Iceberg Cubes with Complex Constraints through Bounding
Lecture Notes in Computer Science, 2003
Data cubes facilitate fast On-Line Analytical Processing (OLAP). Iceberg cubes are special cubes ... more Data cubes facilitate fast On-Line Analytical Processing (OLAP). Iceberg cubes are special cubes comprise only the multidimensional groups satisfying some user-specified constraints. Previous algorithms have focused on iceberg cubes defined by relatively simple constraints such as “COUNT(*) ≥ δ” and “COUNT(*) ≥ δ AND AV G(Profit) ≥ α”. We propose an algorithm I-Cubing that computes iceberg cubes defined by complex constraints involving multiple predicates of aggregates such as “COUNT(*) ≥ δ AND (AV G(Profit) ≥ α OR AV G(profit) ≤ β)”. State-of-the-art iceberg cubing algorithms: BUC cannot handle such cases whereas H-Cubing has to incur extra cost. Our proposed bounding technique can prune for all the given constraints at once without extra cost. Experiments show that bounding has superior pruning power and I-Cubing is twice as fast as H-Cubing. Furthermore, I-Cubing performs equally well with more complex constraints.
2011 IEEE International Conference on Web Services, 2011
The trust of sellers and transactions is a very important issue in e-commerce and e-service envir... more The trust of sellers and transactions is a very important issue in e-commerce and e-service environments. At some e-commerce websites (such as eBay 1 ), the trust management mechanism can compute a trust value of a seller, which is based on the ratings of past transactions given by buyers. This trust value, however, is static and can only reflect the general or global trust status of a seller, and it is not directly bound to a new transaction. As a result, a buyer may be easily cheated by a malicious seller in a new transaction with the notorious value imbalance problem [5], i.e., the malicious seller can build up a good reputation by selling cheap products/services and then start to cheat buyers by selling expensive products/services. Instead of providing such a static trust value, in order to provide more objective trust result for a new potential transaction, a trust evaluation mechanism should based on the ratings of past transactions, the nature of both past transactions and the new transaction. In this paper, we propose a new contextual trust evaluation method. Our method compares the transaction context similarity between the new transaction and past transactions, from which the trust value of the new transaction can be determined. Our method can identify and prevent potentially malicious transactions with the value imbalance problem.
The Star-cubing algorithm performs multiway aggregation on trees but incurs huge memory consumpti... more The Star-cubing algorithm performs multiway aggregation on trees but incurs huge memory consumption. We propose a new algorithm MG-cubing that achieves maximal multiway aggregation. Our experiments show that MG-cubing achieves similar and very often better time and memory efficiency than Star-cubing.
Inferring Aggregation Hierarchies for Integration of Data Marts
Lecture Notes in Computer Science, 2010
The problem of integrating heterogeneous data marts is an important problem in building enterpris... more The problem of integrating heterogeneous data marts is an important problem in building enterprise data warehouses. Specially identifying compatible dimensions is crucial to successful integration. Existing notions of dimension compatibility rely on given and exact dimension hierarchy information being available. In this paper, we propose to infer aggregation hierarchies for dimensions from a database instance and use these inferred aggregation
Association rules describe the degree of dependence between items in transactional datasets by th... more Association rules describe the degree of dependence between items in transactional datasets by their confidences. In this paper, we first introduce the problem of mining top rules, namely those association rules with 100% confidence. Traditional approaches to this problem need a minimum support (minsup) threshold and then can discover the top rules with supports minsup; such approaches, however, rely on minsup to help avoid examining too many candidates and they miss those top rules whose supports are below minsup. The low support top rules (e.g. some unusual combinations of some factors that have always caused some disease) may be very interesting. Fundamentally different from previous work, our proposed method uses a dataset partitioning technique and two border-based algorithms to efficiently discover all top rules with a given consequent, without the constraint of support threshold. Importantly, we use borders to concisely represent all top rules, instead of enumerating them individually. We also discuss how to discover all zero-confidence rules and some very high (say 90%) confidence rules using approaches similar to mining top rules. Experimental results using the Mushroom, the Cleveland heart disease, and the Boston housing datasets are reported to evaluate the efficiency of the proposed approach.
Predicting Disordered Regions in Proteins Based on Decision Trees of Reduced Amino Acid Composition
Journal of Computational Biology, 2006
Intrinsically unstructured proteins (IUPs) are proteins lacking a fixed three dimensional structu... more Intrinsically unstructured proteins (IUPs) are proteins lacking a fixed three dimensional structure or containing long disordered regions. IUPs play an important role in biology and disease. Identifying disordered regions in protein sequences can provide useful information on protein structure and function, and can assist high-throughput protein structure determination. In this paper we present a system for predicting disordered regions in proteins based on decision trees and reduced amino acid composition. Concise rules based on biochemical properties of amino acid side chains are generated for prediction. Coarser information extracted from the composition of amino acids can not only improve the prediction accuracy but also increase the learning efficiency. In cross-validation tests, with four groups of reduced amino acid composition, our system can achieve a recall of 80% at a 13% false positive rate for predicting disordered regions, and the overall accuracy can reach 83.4%. This prediction accuracy is comparable to most, and better than some, existing predictors. Advantages of our approach are high prediction accuracy for long disordered regions and efficiency for large-scale sequence analysis. Our software is freely available for academic use upon request.
In migrating a legacy relational database system to object-oriented(OO) platform, when database m... more In migrating a legacy relational database system to object-oriented(OO) platform, when database migration completes, application modules are to be migrated, where embedded relational database operations are mapped into their OO correspondents. In this paper we study mapping relational update operations to their OO equivalents, which include UPDATE * , INSERT and DELETE operations. Relational update operation translation from relational to OO faces the touchy problem of transformation from a value-based relationship model to a reference-based model and maintaining the relational integrity constraints. Moreover, with a relational database where inheritance is expressed as attribute value subset relationship, changing of some attribute values may lead to the change of the position of an object in the class inheritance hierarchy, which we call object migration.
IEEE Transactions on Knowledge and Data Engineering, 2000
The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy gi... more The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy given aggregation constraints. Pruning unproductive computation for iceberg cubing when nonantimonotone constraints are present is a great challenge because the aggregate functions do not increase or decrease monotonically along the subset relationship between partitions. In this paper, we propose a novel bound prune cubing (BP-Cubing) approach for iceberg cubing with nonantimonotone aggregation constraints. Given a cube over n dimensions, an aggregate for any group-by partition can be computed from aggregates for the most specific n-dimensional partitions (MSPs). The largest and smallest aggregate values computed this way become the bounds for all partitions in the cube. We provide efficient methods to compute tight bounds for base aggregate functions and, more interestingly, arithmetic expressions thereof, from bounds of aggregates over the MSPs. Our methods produce tighter bounds than those obtained by previous approaches. We present iceberg cubing algorithms that combine bounding with efficient aggregation strategies. Our experiments on real-world and artificial benchmark data sets demonstrate that BP-Cubing algorithms achieve more effective pruning and are several times faster than state-of-the-art iceberg cubing algorithms and that BP-Cubing achieves the best performance with the top-down cubing approach.
Determination of Diethylstilbestrol in Aquatic Products by High Performance Liquid Chromatography with Fluorescence Detection
Chinese Journal of Chemistry, 2010
ABSTRACT A sensitive method for the determination of diethylstilbestrol (DES) in aquatic products... more ABSTRACT A sensitive method for the determination of diethylstilbestrol (DES) in aquatic products by high performance liquid chromatography (HPLC) using a fluorescence detector (FLD) was developed. By means of sulfonation in concentrated sulfuric acid and hydrolysis, DES was converted into a reaction product that can emit fluorescence and determined by HPLC. For the first time, HPLC with FLD was applied to the determination of DES and sensitivity comparable to that of LC-MS gained. The limit of detection was 0.1 µg·kg−1, and the recoveries were above 86% with relative standard deviations less than 6.2%. The proposed method was successfully applied to the determination of DES in aquatic products.
Role-based access control (RBAC) has attracted considerable research interest. However, the compu... more Role-based access control (RBAC) has attracted considerable research interest. However, the computational issues of RBAC models are yet to be thoroughly studied. In this paper, we study the problem of efficient maintenance of large RBAC models in a database-based multi-domain Web service environment. We propose first-order (SQL) algorithms to maintain the reachability of access roles under dynamic changes. The main advantages of our algorithms are: the support of various operations required for managing access roles with fractional information of roles; the maintenance of an update through operating a bounded number of join operations despite of the data size. To the best of our knowledge, our algorithms are the first attempt to maintain RBAC models using a first-order language.
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