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View-based query answering in Description Logics: Semantics and complexity

JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2012年第1期78卷 26-46页

作者： Calvanese, Diego De Giacomo, Giuseppe Lenzerini, Maurizio Rosati, Riccardo Free Univ Bozen Bolzano KRDB Res Ctr Knowledge & Data I-39100 Bolzano Italy Univ Roma La Sapienza Dipartimento Informat & Sistemist I-00185 Rome Italy

View-based query answering is the problem of answering a query based only on the precomputed answers to a set of views. While this problem has been widely investigated in databases, it is largely unexplored in the context of Description Logic ontologies. Differently from traditional databases. Description Logics may express several forms of incomplete information, and this poses challenging problems in characterizing the semantics of views. In this paper, we first present a general framework for view-based query answering, where we address the above semantical problems by providing two notions of view-based query answering over ontologies, all based on the idea that the precomputed answers to views are the certain answers to the corresponding queries. We also relate such notions to privacy-aware access to ontologies. Then, we provide decidability results, algorithms, and data complexity characterizations for view-based query answering in several Description Logics, ranging from those with limited modeling capability to highly expressive ones. (C) 2011 Elsevier Inc. All rights reserved.

关键词： View-based query processing Description Logics Ontologies Conjunctive queries Incomplete information Certain answers Privacy-aware data management data complexity DL-Lite family EL-family

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The Key-Dependent Capacity in Multidimensional Linear Cryptanalysis

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COMPUTER JOURNAL 2023年第2期66卷 269-279页

作者： Cao, Wenqin Zhang, Wentao Zhao, Xuefeng Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100093 Peoples R China Shandong Univ Technol Sch Math & Stat Zibo 100093 Peoples R China

The capacity is an important parameter in multidimensional linear attack. In this paper, we firstly explore the distribution of the key-dependent capacity. Based on the magnitude of the correlation contributions, we divide the linear approximations subspace into two sets: one set consists of the strong linear approximations, and the other set consists of the weak linear approximations. We construct two statistics using the linear approximations in the two sets, respectively. Under reasonable assumptions, both of the two statistics follow Gamma distribution. Thus, the capacity is the sum of two statistics that follow Gamma distribution. Secondly, the accuracy of the model is verified by experiments on SMALLPRESENT[4]. Our experimental results show that this model can estimate the variance of the key-dependent capacity more accurately. Thus, we obtain more precise knowledge of the data complexity of the multidimensional linear attack. We derive the upper bound of the data complexity for multidimensional linear attack. Finally, based on our theoretical results, we explore the data complexity of Cho's multidimensional linear attack on PRESENT. Our results are the smallest data complexity for the same round attack so far.

关键词： multidimensional linear attack capacity expected linear probability data complexity

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On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures

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PATTERN RECOGNITION 2009年第11期42卷 2695-2704页

作者： Kim, Sang-Woon Oommen, B. John Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada Univ Agder Grimstad Norway Myongji Univ Dept Comp Sci & Engn Yongin 449728 South Korea

In most pattern recognition (PR) applications, it is advantageous if the accuracy (or error rate) of the classifier can be evaluated or bounded prior to testing it in a real-life setting. It is also well known that if the two class-conditional distributions have a large overlapping volume (almost all the available work on "overlapping of classes" deals with the case when there are only two classes), the classification accuracy is poor. This is because if we intend to use the classification accuracy as a criterion for evaluating a PR system, the points within the overlapping volume tend to lead to maximal misclassification. Unfortunately, the computation of the indices which quantify the overlapping volume is expensive. In this vein, we propose a strategy Of using a prototype reduction scheme (PRS) to approximately, but quickly, compute the latter. In this paper, we demonstrate, first of all, that this is an extremely expedient proposition. Indeed. we show that by completely discarding (we are not aware of any reported scheme which discards "irrelevant" sample (training) points, and which simultaneously attains to an almost-comparable accuracy) the points not included by the PRS, we can obtain a reduced set of sample points, using which, in turn, the measures for the overlapping volume can be computed. The value of the corresponding figures is comparable to those obtained with the original training set (i.e., the one which considers all the data points) even though the computations required to obtain the prototypes and the corresponding measures are significantly less. The proposed method has been rigorously tested on artificial and real-life datasets, and the results obtained are, in our opinion, quite impressive-sometimes faster by two orders of magnitude. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Prototype reduction schemes (PRS), k-nearest neighbor (k-NN) classifier data complexity Class-overlapping

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Metrics for evaluating the performance of joint-transform-correlation-based target recognition and tracking algorithms

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OPTICAL ENGINEERING 2005年第6期44卷 067005-1-067005页

作者： Alam, MS Bal, A Horache, EH Goh, SF Loo, CH Regula, SR Sharma, A Univ S Alabama Dept Elect & Comp Engn Mobile AL 36688 USA

The performance of target detection and tracking algorithms generally depends on the signature, clutter, and noise that are usually present in the input scene. To evaluate the effectiveness of a given algorithm, it is necessary to develop performance metrics based on the input plane as well as output plane information. We develop two performance metrics for assessing the effects of input plane data on the performance of detection and tracking algorithms by identifying three regions of operation-excellent, average, and risky intervals. To evaluate the performance of a given algorithm based on the output plane information, we utilize several metrics that use primarily correlation peak intensity and clutter information. Since the fringe-adjusted joint transform correlation (JTC) was found to yield better correlation output compared to alternate JTC algorithms, we investigate the performance of two fringe-adjusted JTC (FJTC)-based detection and tracking algorithms using several metrics involving the correlation peak sharpness, signal-to-noise ratio, and distortion invariance. The aforementioned input and output plane metrics are used to evaluate the results for both single/multiple target detection and tracking algorithms using real life forward-looking infrared (FLIR) video sequences. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

关键词： target detection and tracking forward-looking infrared image video sequences performance metrics joint transform correlation correlation plane metrics data complexity

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Bayes Imbalance Impact Index: A Measure of Class Imbalanced data Set for Classification Problem

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020年第9期31卷 3525-3539页

作者： Lu, Yang Cheung, Yiu-Ming Tang, Yuan Yan Xiamen Univ Sch Informat Key Lab Sensing & Comp Smart City Xiamen 361005 Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China City Univ Community Coll UOW Coll Hong Kong Fac Sci & Technol Hong Kong Peoples R China Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau Peoples R China

Recent studies of imbalanced data classification have shown that the imbalance ratio (IR) is not the only cause of performance loss in a classifier, as other data factors, such as small disjuncts, noise, and overlapping, can also make the problem difficult. The relationship between the IR and other data factors has been demonstrated, but to the best of our knowledge, there is no measurement of the extent to which class imbalance influences the classification performance of imbalanced data. In addition, it is also unknown which data factor serves as the main barrier for classification in a data set. In this article, we focus on the Bayes optimal classifier and examine the influence of class imbalance from a theoretical perspective. We propose an instance measure called the Individual Bayes Imbalance Impact Index (IBI3) and a data measure called the Bayes Imbalance Impact Index (BI3). IBI3 and BI3 reflect the extent of influence using only the imbalance factor, in terms of each minority class sample and the whole data set, respectively. Therefore, IBI3 can be used as an instance complexity measure of imbalance and BI3 as a criterion to demonstrate the degree to which imbalance deteriorates the classification of a data set. We can, therefore, use BI3 to access whether it is worth using imbalance recovery methods, such as sampling or cost-sensitive methods, to recover the performance loss of a classifier. The experiments show that IBI3 is highly consistent with the increase of the prediction score obtained by the imbalance recovery methods and that BI3 is highly consistent with the improvement in the F1 score obtained by the imbalance recovery methods on both synthetic and real benchmark data sets.

关键词： Indexes complexity theory Learning systems Training Benchmark testing Technological innovation Computer science Bayes classifier class imbalance learning data complexity imbalance measure imbalance recovery methods

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Natural data structure extracted from neighborhood-similarity graphs

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CHAOS SOLITONS & FRACTALS 2019年 119卷 326-331页

作者： Lorimer, Tom Kanders, Karlis Stoop, Ruedi Univ Zurich Inst Neuroinformat Winterthurerstr 190 CH-8057 Zurich Switzerland Swiss Fed Inst Technol Winterthurerstr 190 CH-8057 Zurich Switzerland Swiss Fed Inst Aquat Sci & Technol Eawag Uberlandstr 133 CH-8600 Dubendorf Switzerland Univ Zurich Inst Computat Sci Winterthurerstr 190 CH-8057 Zurich Switzerland Swiss Fed Inst Technol Inst Bldg Mat Stefano Franscini Pl 3 CH-8093 Zurich Switzerland

'Big' high-dimensional data are commonly analyzed in low-dimensions, after performing a dimensionality reduction step that inherently distorts the data structure. For a similar analysis, clustering methods are also often used. These methods introduce a bias as well, either by starting from the assumption of a particular, often geometric, property of the clusters, or by using iterative schemes to enhance cluster contours, with consequences that are hard to control. The goal of data analysis should, however, be to encode and detect structural data features at all scales and densities simultaneously, without assuming a parametric form of data point distances, or modifying them. Here, we propose a novel approach that directly encodes data point neighborhood similarities as a sparse graph. Our non-iterative framework permits a transparent interpretation of data, without altering the original data dimension and metric. Several natural and synthetic data applications demonstrate the efficacy of our novel method. (C) 2019 Published by Elsevier Ltd.

关键词： data complexity data networks Big data Clustering

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Improved linear cryptanalysis on 25-round SMS4

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IET COMMUNICATIONS 2022年第14期16卷 1643-1653页

作者： Fu, Lishi Jin, Chenhui PLA SSF Informat Engn Univ Dept Cryptog Engn Zhengzhou 450001 Peoples R China Sci Ave Zhengzhou Henan Peoples R China

SMS4 is the Chinese national standard for WLAN, which is also an ISO/IEC international standard. This paper presents a linear cryptanalysis on 25-round SMS4 for the first time. Firstly, this paper adopts a new statistic in linear cryptanalysis, which is aimed to compute the distance between one cipher system and one random permutation, which can be used to distinguish the right key from the wrong keys by the different probability distributions they follow. Secondly, the attack principal and data complexity are given when this new statistic is used in multiple linear cryptanalysis. Thirdly, this paper presents 21-round linear approximations with absolute correlation 2-62${2<^>{ - 62}}$ by fixing the least significant input bit of the Sbox with nonzero mask in the first round to be 1. Furthermore, this paper gives 48 21-round linear approximations with absolute correlation 2-62${2<^>{ - 62}}$. Based on the restrictions between those 48 linear approximations and fast Fourier transformation technique, the whole attack needs 2125.62${2<^>{125.62}}$ known plaintexts, 2126.56${2<^>{126.{\rm{56}}}}$25-round SMS4 encryptions along with 2113.75${{\rm{2}}<^>{{\rm{113}}{\rm{.75}}}}$ memory accesses, and 2111.7${{\rm{2}}<^>{{\rm{111}}{\rm{.7}}}}$ bytes of memory, which is the best cryptanalysis result on SMS4 so far. Finally, this paper generalizes the linear cryptanalysis method with conditioned linear approximation, which can also be adopted in attacking other ciphers.

关键词： multiple linear cryptanalysis cryptography approximation theory Chinese national standard absolute correlation 2−622 probability SMS4 encryptions linear cryptanalysis method data complexity cryptanalysis result 21-round linear approximations improved linear cryptanalysis 48 linear approximations conditioned linear approximation fast Fourier transforms wireless LAN

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Reconciling Description Logics and Rules

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JOURNAL OF THE ACM 2010年第5期57卷 30-30.62页

作者： Motik, Boris Rosati, Riccardo Univ Oxford Comp Lab Oxford OX1 3QD England Univ Roma La Sapienza Dipartimento Informat & Sistemist I-00185 Rome Italy

Description logics (DLs) and rules are formalisms that emphasize different aspects of knowledge representation: whereas DLs are focused on specifying and reasoning about conceptual knowledge, rules are focused on nonmonotonic inference. Many applications, however, require features of both DLs and rules. Developing a formalism that integrates DLs and rules would be a natural outcome of a large body of research in knowledge representation and reasoning of the last two decades;however, achieving this goal is very challenging and the approaches proposed thus far have not fully reached it. In this paper, we present a hybrid formalism of MKNF+ knowledge bases, which integrates DLs and rules in a coherent semantic framework. Achieving seamless integration is nontrivial, since DLs use an open-world assumption, while the rules are based on a closed-world assumption. We overcome this discrepancy by basing the semantics of our formalism on the logic of minimal knowledge and negation as failure (MKNF) by Lifschitz. We present several algorithms for reasoning with MKNF+ knowledge bases, each suitable to different kinds of rules, and establish tight complexity bounds.

关键词： Theory Description logics answer set programming combined complexity data complexity

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Visual decision support for business ecosystem analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 65卷 271-282页

作者： Basole, Rahul C. Huhtamaki, Jukka Still, Kaisa Russell, Martha G. Georgia Inst Technol Tennenbaum Inst 85 Fifth St NW Atlanta GA 30332 USA Georgia Inst Technol Sch Interact Comp 85 Fifth St NW Atlanta GA 30332 USA Tampere Univ Technol Dept Math Tampere Finland VTT Finland Espoo Finland Stanford Univ mediaX Stanford CA 94305 USA Stanford Univ H STAR Stanford CA 94305 USA

This study comparatively evaluates the effectiveness of three visualization methods (list, matrix, network) and the influence of data complexity, task type, and user characteristics on decision performance in the context of business ecosystem analysis. We pursue this objective using an exploratory study with 14 prototypical users (e.g. executives, analysts, investors, and policy makers). The results show that in low complexity contexts, decision performance between visual representations differ but not substantially. In high complexity contexts, however, decision performance suffers significantly if visual representations are not appropriately matched to task types. Our study makes several theoretical and practical contributions. Theoretically, we extend cognitive fit theory by investigating the impact of business ecosystem task type and complexity. Managerially, our study contributes to the relatively underexplored, but emerging area of the design of business ecosystem intelligence tools and presentation of business ecosystem data for the purpose of decision making. We conclude with future research opportunities. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Information visualization Decision support Business ecosystem Cognitive fit theory data complexity

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Meta-learning recommendation of default size of classifier pool for META-DES

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NEUROCOMPUTING 2016年 216卷 351-362页

作者： Roy, Anandarup Cruz, Rafael M. O. Sabourin, Robert Cavalcanti, George D. C. Univ Quebec Ecole Technol Super Montreal PQ Canada Univ Fed Pernambuco Ctr Informat Recife PE Brazil

Dynamic ensemble selection (DES) is a mechanism for selecting an ensemble of competent classifiers from a pool of base classifiers, in order to classify a particular test sample. The size of this pool is user defined, and yet is crucial for controlling the computational complexity and performance of a DES. An appropriate pool size depends on the choice of base classifiers, the underlying DES method used, and more importantly, the characteristics of the given problem. After the DES method and the base classifiers are selected, an appropriate pool size for a given problem can be obtained by the repetitive application of the DES with a variety of sizes, after which a selection is performed. Since this brute force approach is computationally expensive, researchers set the pool size to a pre-specified value. This strategy, may, however further complicate and reduce the performance of the DES method. Instead, we propose a framework that is akin to meta-learning, in order to predict a suitable pool size based on the intrinsic classification complexity of a problem. In our strategy, we collect meta-features corresponding to classification complexity from a number of data sets. Additionally, we obtain the best pool sizes for these data sets using the brute force approach. The association between these two pieces of information is captured using meta-regression models. Finally, for an unseen problem, we predict the pool size using this model and the classification complexity information. We carry out experiments on 65 two-class data sets and with a recent DES method, namely, META-DES. We also consider variants of meta-regression techniques and report prediction results, after which we carry out a statistical comparison among them. Moreover, we investigate the performance of META-DES and observe that it performs equivalently for both the predicted and the best pool sizes. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Dynamic ensemble selection META-DES Meta-learning Meta-regression data complexity

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