Given two data matrices X and Y, Sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors u and v to maximize the correlation between Xu and Y v. However, classical and sparse CCA models co...
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In this study we have modeled a cyber security situation and demonstrated the system level effects using system dynamics PoC (Proof-of-Concept) model. We modeled a hypothetical Company's Information Technology inf...
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ISBN:
(纸本)9781538661345
In this study we have modeled a cyber security situation and demonstrated the system level effects using system dynamics PoC (Proof-of-Concept) model. We modeled a hypothetical Company's Information Technology infrastructure and simulated a Distributed Denial-of-Service attack on one part of the system in an effort to see how other parts of the system were affected. To validate our simulation model, we developed a cybersecurity testbed and conducted a cyber-attack on one application and observed its impact on the whole system. The results indicate that by using known vulnerabilities and the current knowledge about infrastructure and security controls, the system dynamic cyber security simulation modeling allows an organization to imitate the attacker activities and helps to assess and mitigate the system's risk exposure.
An important product measure to determine the effectiveness of software processes is the defect density (DD). In this study, we propose the application of support vector regression (SVR) to predict the DD of new softw...
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To ensure the quality of current highly configurable softwaresystems, intensive testing is needed to test all the configuration combinations and detect all the possible faults. This task becomes more challenging for ...
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To ensure the quality of current highly configurable softwaresystems, intensive testing is needed to test all the configuration combinations and detect all the possible faults. This task becomes more challenging for most modern softwaresystems when constraints are given for the configurations. Here, intensive testing is almost impossible, especially considering the additional computation required to resolve the constraints during the test generation process. In addition, this testing process is exhaustive and time-consuming. Combinatorial interaction strategies can systematically reduce the number of test cases to construct a minimal test suite without affecting the effectiveness of the tests. This paper presents a new efficient search-based strategy to generate constrained interaction test suites to cover all possible combinations. The paper also shows a new application of constrained interaction testing in software fault searches. The proposed strategy initially generates the set of all possible t − tuple combinations;then, it filters out the set by removing the forbidden t − tuples using the base forbidden tuple (BFT) approach. The strategy also utilizes a mixed neighborhood tabu search (TS) to construct optimal or near-optimal constrained test suites. The efficiency of the proposed method is evaluated through a comparison against two well-known state-of-the-art tools. The evaluation consists of three sets of experiments for 35 standard benchmarks. Additionally, the effectiveness and quality of the results are assessed using a real-world case study. Experimental results show that the proposed strategy outperforms one of the competitive strategies, ACTS, for approximately 83% of the benchmarks and achieves similar results to CASA for 65% of the benchmarks when the interaction strength is 2. For an interaction strength of 3, the proposed method outperforms other competitive strategies for approximately 60% and 42% of the benchmarks. The proposed strategy can also
Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equ...
Crime forecasting has been one of the most complex challenges in law enforcement today, especially when an analysis tends to evaluate inferable and expanded crime rates, although a few methodologies for subsequent equivalents have been embraced before. In this work, we use a strategy for a time series model and machine testing systems for crime estimation. The paper centers on determining the quantity of crimes. Considering various experimental analyses, this investigation additionally features results obtained from a neural system that could be a significant alternative to machine learning and ordinary stochastic techniques. In this paper, we applied various techniques to forecast the number of possible crimes in the next 5 years. First, we used the existing machine learning techniques to predict the number of crimes. Second, we proposed two approaches, a modified autoregressive integrated moving average model and a modified artificial neural network model. The prime objective of this work is to compare the applicability of a univariate time series model against that of a variate time series model for crime forecasting. More than two million datasets are trained and tested. After rigorous experimental results and analysis are generated, the paper concludes that using a variate time series model yields better forecasting results than the predicted values from existing techniques. These results show that the proposed method outperforms existing methods.
It is recognized that the semantic space of knowledge is a hierarchical concept network. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic relations via machin...
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ISBN:
(纸本)9781509038473
It is recognized that the semantic space of knowledge is a hierarchical concept network. This paper presents theories and algorithms of hierarchical concept classification by quantitative semantic relations via machine learning based on concept algebra. The equivalence between formal concepts are analyzed by an Algorithm of Concept Equivalence Analysis (ACEA), which quantitatively determines the semantic similarity of an arbitrary pair of formal concepts. This leads to the development of the Algorithm of Relational Semantic Classification (ARSC) for hierarchically classify any given concept in the semantic space of knowledge. Experiments applying Algorithms ACEA and ARSC on 20 formal concepts are successfully conducted, which encouragingly demonstrate the deep machine understanding of semantic relations and their quantitative weights beyond human perspectives on knowledge learning and natural language processing.
Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements pos...
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Dynamical sampling deals with signals that evolve in time under the action of a linear operator. The purpose of the present paper is to analyze the performance of the basic dynamical sampling algorithms in the finite ...
Dynamical sampling deals with signals that evolve in time under the action of a linear operator. The purpose of the present paper is to analyze the performance of the basic dynamical sampling algorithms in the finite dimensional case and study the impact of additive noise. The algorithms are implemented and tested on synthetic and real data sets, and denoising techniques are integrated to mitigate the effect of the noise. We also develop theoretical and numerical results that validate the algorithm for recovering the driving operators, which are defined via a real symmetric convolution.
We present an approach to social reasoning that integrates prior work on norms and obligations with the BDI approach to agent architectures. Norms and obligations can be used to increase the efficiency of agent reason...
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We present an approach to social reasoning that integrates prior work on norms and obligations with the BDI approach to agent architectures. Norms and obligations can be used to increase the efficiency of agent reasoning, and their explicit representation supports reasoning about a wide range of behaviour types in a single framework. We propose a modified BDI interpreter loop that takes norms and obligations into account in an agent's deliberation.
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