An improved lossless data hiding scheme based on image VQ-index residual value coding

Copyright protection and information security have become serious problems due to the ever growing amount of digital data over the Internet. Reversible data hiding is a special type of data hiding technique that guarantees not only the secret data but also the cover media can be reconstructed without any distortion. Traditional schemes are based on spatial, discrete cosine transformation (DCT) and discrete wavelet transformation (DWT) domains. Recently, some vector quantization (VQ) based reversible data hiding schemes have been proposed. This paper proposes an improved reversible data hiding scheme based on VQ-index residual value coding. Experimental results show that our scheme outperforms two recently proposed schemes, namely side-match vector quantization (SMVQ)-based data hiding and modified fast correlation vector quantization (MFCVQ)-based data hiding.     

Minimal explanations of missing values by chasing acquisitional data

Acquisitional issues widely exist in GPS and sensor networks. They pertain to where, when, and how often data is physically acquired (sampled) and delivered to some query processing systems. Due to the dynamic environment that the data is acquired with the change of monitoring time, acquisitional data are typically a time-stamped stream where a time-stamped value could either contain noises or be missing. Aiming to improve the quality of data acquisition, we focus on the explanations of missing values in this paper. Several techniques have been developed to provide the explanation on relational data, however, they cannot be directly applied in acquisitional stream data due to its dynamic feature, such as the “change” of acquisitional stream data between two adjacent monitoring time is often constrained by some rules. We show that an explanation could be incorrect or unreasonable if those constraints are not taken into account. We propose one novel chasing technique by considering both spatial and temporal correlations to explain missing values and guarantee a minimal explanation. Experimental results show that our approach can efficiently return high-quality and minimal explanations of missing values.     

A unifying criterion for unsupervised clustering and feature selection

Exploratory data analysis methods are essential for getting insight into data. Identifying the most important variables and detecting quasi-homogenous groups of data are problems of interest in this context. Solving such problems is a difficult task, mainly due to the unsupervised nature of the underlying learning process. Unsupervised feature selection and unsupervised clustering can be successfully approached as optimization problems by means of global optimization heuristics if an appropriate objective function is considered. This paper introduces an objective function capable of efficiently guiding the search for significant features and simultaneously for the respective optimal partitions. Experiments conducted on complex synthetic data suggest that the function we propose is unbiased with respect to both the number of clusters and the number of features.     

Function point measurement from Web application source code based on screen transitions and database accesses

A function point (FP) is a unit of measurement that expresses the degree of functionality that an information system provides to a user. Many software organizations use FPs to estimate the effort required for software development. However, it is essential that the definition of 1 FP be based on the software development experience of the organization. In the present study, we propose a method by which to automatically extract data and transaction functions from Web applications under several conditions using static analysis. The proposed method is based on the International Function Point Users Group (IFPUG) method and has been developed as an FP measurement tool. We applied the proposed method to several Web applications and examined the difference between FP counts obtained by the tool and those obtained by a certified FP specialist (CFPS). The results reveal that the numbers of data and transaction functions extracted by the tool is approximately the same as the numbers of data and transaction functions extracted by the specialist.     

On the estimation of adequate test set size using fault failure rates

Test set size in terms of the number of test cases is an important consideration when testing software systems. Using too few test cases might result in poor fault detection and using too many might be very expensive and suffer from redundancy. We define the failure rate of a program as the fraction of test cases in an available test pool that result in execution failure on that program. This paper investigates the relationship between failure rates and the number of test cases required to detect the faults. Our experiments based on 11 sets of C programs suggest that an accurate estimation of failure rates of potential fault(s) in a program can provide a reliable estimate of adequate test set size with respect to fault detection and should therefore be one of the factors kept in mind during test set construction. Furthermore, the model proposed herein is fairly robust to incorrect estimations in failure rates and can still provide good predictive quality. Experiments are also performed to observe the relationship between multiple faults present in the same program using the concept of a failure rate. When predicting the effectiveness against a program with multiple faults, results indicate that not knowing the number of faults in the program is not a significant concern, as the predictive quality is typically not affected adversely.     

Simulations, models, and testbeds: A mutual catalysis

Recent developments in the area of decentralized and infrastructureless systems opened avenues for novel applications. Along with these new technologies, new questions regarding their operational bounds in terms of e.g. scalability and security arose. Due to the sparse presence of real-world decentralized and infrastructureless systems, new protocols and applications have to be scrutinized by means of simulation, in (small-scale) testbeds, and by analytical models. In this paper, we discuss challenges of evaluating security mechanisms for mobile ad hoc networks and peer-to-peer systems. We focus on harmonizing predictions of analytical models and results obtained from simulation studies and testbed experiments.     

Approximate steady-state analysis of large Markov models based on the structure of their decision diagram encoding

We propose a new approximate numerical algorithm for the steady-state solution of general structured ergodic Markov models. The approximation uses a state-space encoding based on multiway decision diagrams and a transition rate encoding based on a new class of edge-valued decision diagrams. The new method retains the favorable properties of a previously proposed Kronecker-based approximation, while eliminating the need for a Kronecker-consistent model decomposition. Removing this restriction allows for a greater utilization of event locality, which facilitates the generation of both the state-space and the transition rate matrix, thus extends the applicability of this algorithm to larger and more complex models.     

Directional neighbor discovery in 60 GHz indoor wireless networks

Signal propagation in the 60 GHz band significantly differs from that in the 2.4 and 5 GHz bands. In particular, the signals are often reflected in indoor settings in this band. Directional antennas can help limit the impact of reflection, but make the process of neighbor discovery complex. We consider this problem in this paper. We examine two approaches (a) direct discovery where each node explicitly discovers its neighbors, and (b) gossip-based discovery where nodes exchange information about their already discovered neighbors. We analyze the two approaches and validate our models via simulations. We examine the impact of system parameters such as varying beamwidth and node density.     

Scheduling gain for frequency-selective Rayleigh-fading channels with application to self-organizing packet scheduling

This paper investigates packet scheduling in the context of Self-Optimizing Networks, and demonstrates how to improve coverage dynamically by adjusting the scheduling strategy. We focus on α-fair schedulers, and we provide methods for calculating the scheduling gain, including several closed form formulas. Scheduling gain is analyzed for different fading models, with a particular focus on the frequency-selective channel. We then propose a coverage-capacity self-optimization algorithm based on α-fair schedulers. A use case illustrates the implementation of the algorithm and simulation results show that important coverage gains are achieved at the expense of very little computing power.     

Decision support for Cybersecurity risk planning

Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions.