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In this paper, we proposes high payload reversible data hiding scheme using weighted matrix. Generate cover image by enlarge the size of original image using interpolation. Partition the original image into (3 × 3) pixel blocks and cover image into (5 × 5) blocks. Perform modular sum of entry-wise multiplication of an (3 × 3) original pixel block with same size shared predefined weighted matrix W . At most twelve multiplication operations are performed to embed 48 bits secret data within an (5 × 5) overlapped pixel block. In each operation, the data embedding position are identified and stored the positional value by modifying three least significant bits of the interleaved pixel of cover image to hide large amount of secret data. For i -th block i = 1, 2, …, we update the weighted matrix W i +1 as W i +1 = ( W i × κ − 1) mod 9, where gcd ( κ , 9) = 1. The original pixels are not effected during data embedding in our scheme which assure reversibility. The proposed scheme provides average embedding payload 2.97 bits per pixel (bpp) with good visual quality measured by peak signal to noise ratio (PSNR) guaranteed to be higher than 37.97 dB. Finally, we compare our scheme with other state-of-the-art methods and obtain reasonably better performance in terms of data embedding capacity. [ABSTRACT FROM AUTHOR]

Pattern set mining entails discovering groups of frequent itemsets that represent potentially relevant knowledge. Global constraints are commonly enforced to focus the analysis on most interesting pattern sets. However, these constraints evaluate and select each pattern set individually based on its itemset characteristics. This paper extends traditional global constraints by proposing a novel constraint, called schema-based constraint, tailored to relational data. When coping with relational data itemsets consist of sets of items belonging to distinct data attributes, which constitute the itemset schema. The schema-based constraint allows us to effectively combine all the itemsets that are semantically correlated with each other into a unique pattern set, while filtering out those pattern sets covering a mixture of different data facets or giving a partial view of a single facet. Specifically, it selects all the pattern sets that are (i) composed only of frequent itemsets with the same schema and (ii) characterized by maximal size among those corresponding to that schema. Since existing approaches are unable to select one representative pattern set per schema in a single extraction, we propose a new Apriori-based algorithm to efficiently mine pattern sets satisfying the schema-based constraint. The experimental results achieved on both real and synthetic datasets demonstrate the efficiency and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper proposes a combined canonical variate analysis (CVA) and Fisher discriminant analysis (FDA) scheme (denoted as CVA–FDA) for fault diagnosis, which employs CVA for pretreating the data and subsequently utilizes FDA for fault classification. In addition to the improved handling of serial correlations in the data, the utilization of CVA in the first step provides similar or reduced dimensionality of the pretreated datasets compared with the original datasets, as well as decreased degree of overlap. The effectiveness of the proposed approach is demonstrated on the Tennessee Eastman process. The simulation results demonstrate that (i) CVA–FDA provides better and more consistent fault diagnosis than FDA, especially for data rich in dynamic behavior; and (ii) CVA–FDA outperforms dynamic FDA in both discriminatory power and computational time. [ABSTRACT FROM AUTHOR]

Current approaches for monitoring the process correlation structure lag significantly behind the effectiveness already achieved on the detection of changes in the mean levels of process variables. We demonstrate that this is true, even for well-known methodologies such as MSPC-PCA and related approaches. On the other hand, data-driven process monitoring approaches are typically non-causal and based on the marginal covariance between process variables. We also show that such global measure of association is unable, by design, to effectively discern changes in the local correlation structure of the system and propose, for the first time, the explicit use of partial correlations in process monitoring. As a second contribution, we introduce the use of sensitivity enhancing data transformations (SET) with the ability to maximize the detection ability of all monitoring procedures based on (partial or marginal) correlation, and show how they can be constructed. Results confirm the added-value of the proposed monitoring scheme. [ABSTRACT FROM AUTHOR]

The Nearest Neighbor rule is one of the most successful classifiers in machine learning. However, it is very sensitive to noisy, redundant and irrelevant features, which may cause its performance to deteriorate. Feature weighting methods try to overcome this problem by incorporating weights into the similarity function to increase or reduce the importance of each feature, according to how they behave in the classification task. This paper proposes a new feature weighting classifier, in which the computation of the weights is based on a novel idea combining imputation methods – used to estimate a new distribution of values for each feature based on the rest of the data – and the Kolmogorov–Smirnov nonparametric statistical test to measure the changes between the original and imputed distribution of values. This proposal is compared with classic and recent feature weighting methods. The experimental results show that our feature weighting scheme is very resilient to the choice of imputation method and is an effective way of improving the performance of the Nearest Neighbor classifier, outperforming the rest of the classifiers considered in the comparisons. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a steganographic scheme based on the varieties of coefficients of discrete cosine transformation of an image. The major problem of hiding data in the high-frequency coefficients of discrete cosine transformation is that rounding errors will be added into the spatial-domain image, and thus cannot be transformed back to the correct modified coefficients of the discrete cosine transformation. To solve this problem, we use integer mapping to implement our discrete cosine transformation. Thus, the image recovered from the modified coefficients can be transformed again to the correct data hidden coefficients. [Copyright &y& Elsevier]

Accurate boundary detection is a critical step of the image segmentation process. While most edge detectors rely on the presence of strong intensity gradients, this criteria can limit robustness in many real world cases. In this work we propose a scheme which makes use of a combination of both global and local segmentation methods to capture the boundary of target objects in low contrast images. This approach has several advantages: the globally convex segmentation scheme is immune from initial conditions and is easily adapted to the data. The addition of a segmentation scheme based on local curve evolution produces a solution which is shown to help preserve topology between the initial and target shape, a property lacking in globally convex segmentation schemes. Experimental results show that the proposed method achieves enhanced performance compared to classical data-driven segmentation schemes proposed in the literature. [ABSTRACT FROM AUTHOR]

Abstract: Recently, Universum data that does not belong to any class of the training data, has been applied for training better classifiers. In this paper, we address a novel boosting algorithm called AdaBoost that can improve the classification performance of AdaBoost with Universum data. AdaBoost chooses a function by minimizing the loss for labeled data and Universum data. The cost function is minimized by a greedy, stagewise, functional gradient procedure. Each training stage of AdaBoost is fast and efficient. The standard AdaBoost weights labeled samples during training iterations while AdaBoost gives an explicit weighting scheme for Universum samples as well. In addition, this paper describes the practical conditions for the effectiveness of Universum learning. These conditions are based on the analysis of the distribution of ensemble predictions over training samples. Experiments on handwritten digits classification and gender classification problems are presented. As exhibited by our experimental results, the proposed method can obtain superior performances over the standard AdaBoost by selecting proper Universum data. [Copyright &y& Elsevier]

Abstract: Cloud computing poses many challenges on integrity and privacy of users’ data though it brings an easy, cost-effective and reliable way of data management. Hence, secure and efficient methods are needed to ensure integrity and privacy of data stored at the cloud. Wang et al. proposed a privacy-preserving public auditing protocol in 2010 but it is seriously insecure. Their scheme is vulnerable to attacks from malicious cloud server and outside attackers regarding to storage correctness. So they proposed a scheme in 2011 with an improved security guarantee but it is not efficient. Thus, in this paper, we proposed a scheme which is secure and with better efficiency. It is a public auditing scheme with third party auditor (TPA), who performs data auditing on behalf of user(s). With detail security analysis, our scheme is proved secure in the random oracle model and our performance analysis shows the scheme is efficient. [Copyright &y& Elsevier]

Highlights: [•] We study previous works about datasets on bankruptcy prediction and credit scoring. [•] We propose a procedure to improve the results of previous works. [•] We show that Bagging scheme must be used on the correct base procedures. [•] Our proposal is simpler and improves complex procedures. [ABSTRACT FROM AUTHOR]

Abstract: We describe how oriented Basic Image Feature Columns (oBIF Columns) can be used for writer identification and how this texture-based scheme can be enhanced by encoding a writer's style as the deviation from the mean encoding for a population of writers. We hypothesise that this deviation, the Delta encoding, provides a more informative encoding than the texture-based encoding alone. The methods have been evaluated using the IAM dataset and by making entries to two top international competitions for assessing the state-of-the-art in writer identification. We demonstrate that the oBIF Column scheme on its own is sufficient to gain a performance level of 99% when tested using 300 writers from the IAM dataset. However, on the more challenging competition datasets, significantly improved performance was obtained using the Delta encoding scheme, which achieved first place in both competitions. In our characterisation of the Delta encoding, we demonstrate that the method is making use of information contained in the correlation between the written style of different textual elements, which may not be used by other methods. [Copyright &y& Elsevier]

Abstract: Kriging is well-known, frequently applied method in geo-statistics. Its success primarily depends on the total number of measurements for some sample points. If there are sufficient sample points with measurements, kriging will reflect the surface accurately. Obtaining a sufficient number of measurements can be costly and time-consuming. Thus, different companies might obtain a limited number of measurements of the same region and want to offer predictions collaboratively. However, due to privacy concerns, they might hesitate to cooperate with each other. In this paper, we propose a protocol to estimate kriging-based predictions using partitioned data from two parties while preserving their confidentiality. Our protocol also protects a client’s privacy. The proposed method helps two servers create models based on split data without divulging private data and provide predictions to their clients while preserving the client’s confidentiality. We analyze the scheme with respect to privacy, performance, and accuracy. Our theoretical analysis shows that it achieves privacy. Although it causes some additional costs, they are not critical to overall performance. Our real data-based empirical outcomes show that our method is able to offer accurate predictions even if there are accuracy losses due to privacy measures. [Copyright &y& Elsevier]

Abstract: Collecting sensory data using a mobile data sink has been shown to drastically reduce energy consumption at the cost of increasing delivery delay. Towards improved energy-latency trade-offs, we propose a biased, adaptive sink mobility scheme, that adjusts to local network conditions, such as the surrounding density, remaining energy and the number of past visits in each network region. The sink moves probabilistically, favoring less visited areas in order to cover the network area faster, while adaptively stopping more time in network regions that tend to produce more data. We implement and evaluate our mobility scheme via simulation in diverse network settings. Compared to known blind random, non-adaptive schemes, our method achieves significantly reduced latency, especially in networks with non-uniform sensor distribution, without compromising the energy efficiency and delivery success. [Copyright &y& Elsevier]

Highlights: [•] Ever-growing computer resources have opened the door to Data-Enabled Design. [•] Frameworks are proposed for the Data-Enabled analysis of tall buildings. [•] The efficiency of spectral POD in defining reduced order frameworks is shown. [•] A novel Data-Enabled large-scale optimization scheme is proposed for tall buildings. [•] The efficiency of the proposed schemes is shown on a representative case study. [ABSTRACT FROM AUTHOR]

Abstract: A clock-recovery scheme is proposed for the burst-mode optical data transmission using 4-level pulse-amplitude modulation in passive optical networks. It utilizes the nonlinear relaxation oscillation from a directly modulated laser to generate a clock tone at the transmitter. At the receiver side, it employs an injection locked oscillator (ILO) to lock to the clock tone and generates a full clock. A band-pass-filter is inserted before the ILO to reduce the jitter in the generated clock. [Copyright &y& Elsevier]

Abstract: Unlike traditional video steganography, motion-vectors-based steganographic approaches embed messages by modifying the motion vectors of the cover video. The scheme may produce little quality degradation of the stego video and little influence on the statistical characteristics of the spatial or frequency coefficients of the frames. As a result, the existing video steganalytic algorithms based on the statistical features of frames or the correlation between adjacent frames cannot effectively detect the motion-vectors-based steganographic system. In this paper, an improved steganalytic method against motion-vectors-based steganography is proposed. Based on the proposed algorithm, the correlations between motion vectors both in spatial and temporal domain are effectively quantified and a novel 12 dimensional statistical feature vector is extracted. The support vector machine (SVM) is trained with these vectors to detect the presence of the hidden data. Compared with other algorithms, the proposed scheme has higher detection accuracy for the typical motion-vectors-based steganographic algorithms. [Copyright &y& Elsevier]

Abstract: In verifiable multi-secret sharing schemes (VMSSs), many secrets can be shared but only one share is kept by each user and this share is verifiable by others. In this paper, we propose two secure, efficient, and verifiable multi-secret sharing schemes, namely Scheme-I and Scheme-II. Scheme-I is based on the Lagrange interpolating polynomial and the LFSR-based public key cryptosystem. The Lagrange interpolating polynomial is used to split and reconstruct the secrets and the LFSR-based public key cryptosystem is employed to verify the validity of the data. Scheme-II is designed according to the LFSR sequence and the LFSR-based public key cryptosystem. We compare our schemes with the state-of-the-art in terms of attack resistance, computation complexity, and so on, and conclude that our schemes have better performance and incur less computation overhead. Our schemes can effectively detect a variety of forgery or cheating actions to ensure that the recovery of the secrets is secure and creditable, and the length of the private key is only one third of that of others for the same security level. [Copyright &y& Elsevier]

Abstract: There have been numerous techniques proposed in the literature that aim to improve the performance of cache memories by reducing cache conflicts. These techniques were proposed over the past decade and each proposal independently claimed to reduce conflict misses. However, because the published results used different benchmarks and different experimental setups, it is not easy to compare them. In this paper we report a side-by-side comparison of these techniques. We also evaluate the suitability of some of these techniques for caches with higher set associativities. In addition to evaluating techniques for their impact on cache misses and average memory access times, we also evaluate the techniques for their ability in reducing the non-uniformity of cache accesses. The conclusion of our work is that, each application may benefit from a different technique and no single scheme works universally well for all applications. We also observe that, for the majority of applications, XORing (XOR) and Odd-multiplier indexing schemes perform reasonably well. Among programmable associativity techniques, B-cache performs better than column-associative and adaptive-caches, but column-associative caches require very minimal extensions to hardware. Uniformity of cache accesses is improved most by B-cache technique while column-associative cache also improves cache access uniformities. Based on the observation that different techniques benefit different applications, we explored the use of multiple, programmable addressing mechanisms, each addressing scheme designed for a specific application. We include some preliminary data using multiple addressing schemes. [Copyright &y& Elsevier]

Abstract: The data warehouse design task needs to consider both the end-user requirements and the organization data sources. For this reason, the data warehouse design has been traditionally considered a reengineering process, guided by requirements, from the data sources. Most current design methods available demand highly-expressive end-user requirements as input, in order to carry out the exploration and analysis of the data sources. However, the task to elicit the end-user information requirements might result in a thorough task. Importantly, in the data warehousing context, the analysis capabilities of the target data warehouse depend on what kind of data is available in the data sources. Thus, in those scenarios where the analysis capabilities of the data sources are not (fully) known, it is possible to help the data warehouse designer to identify and elicit unknown analysis capabilities. In this paper we introduce a user-centered approach to support the end-user requirements elicitation and the data warehouse multidimensional design tasks. Our proposal is based on a reengineering process that derives the multidimensional schema from a conceptual formalization of the domain. It starts by fully analyzing the data sources to identify, without considering requirements yet, the multidimensional knowledge they capture (i.e., data likely to be analyzed from a multidimensional point of view). Next, we propose to exploit this knowledge in order to support the requirements elicitation task. In this way, we are already conciliating requirements with the data sources, and we are able to fully exploit the analysis capabilities of the sources. Once requirements are clear, we automatically create the data warehouse conceptual schema according to the multidimensional knowledge extracted from the sources. [ABSTRACT FROM AUTHOR]

Abstract: Typical wireless sensor network comprises sensors and a sink. The sink gathers and analyzes the data from sensors and then takes the corresponding action. Such architecture may have single-point of failure problem. Besides, when sensors have no direct link with the sink, they must deliver data by hopping scheme. The sink may take a longer time to collect data. Furthermore, faulty sensors and environment interference may cause an incorrect result. To overcome the mentioned problems, we propose a consensus problem based solution to make all sensors reach an agreement without the sink. It can conduct actions more quickly and directly. [Copyright &y& Elsevier]