In the recent era the growth of technology is tremendous and at the same time, the misuse of technology is also increasing with an equal scale. Thus the owners have to protect the multimedia data from the malicious and piracy. This has led the researchers to the new era of cryptography and watermarking. In the traditional security algorithm for the audio, the algorithm is implemented on the digital data after the traditional analog to digital conversion. But in this article, we propose the crypto – watermarking algorithm based on sparse sampling to be implemented during the analog to digital conversion process only. The watermark is generated by exploiting the structure of HAAR transform. The performance of the algorithm is tested on various audio signals and the obtained SNR is greater than 30dB and the algorithm results in good robustness against various signal attacks such as echo addition, noise addition, reverberation etc.

Uninitialized variables have been a source of errors since the beginning of software engineering. Some programming languages (e.g. Java and Python) will automatically zero-initialize such variables, but others, like C and C++, leave their state undefined. While laying aside initialization in C and C++ might be a performance advantage if an initial value can't be supplied, working with such variables is an undefined behavior, and is a common source of instabilities and crashes. To avoid such errors, whenever meaningful initialization is possible, it should be used. Tools for detecting these errors run time have existed for decades, but those require the problematic code to be executed. Since in many cases the number of possible execution paths are combinatoric, static analysis techniques emerged as an alternative. In this paper, we overview the technique for detecting uninitialized C++ variables using the Clang Static Analyzer, and describe various heuristics to guess whether a specific variable was left in an undefined state intentionally. We implemented a prototype tool based on our idea and successfully tested it on large open source projects.

Pixel Grouping (PG) of digital images has been a key consideration in recent development of the Reversible Data Hiding (RDH) schemes. While a PG kernel with neighborhood pixels helps compute image groups for better embedding rate-distortion performance, only horizontal neighborhood pixel group of size 1×3 has so far been considered. In this paper, we formulate PG kernels of sizes 3×1, 2×3 and 3×2 and investigate their effect on the rate-distortion performance of a prominent PG-based RDH scheme. Specially, a kernel of size 3×2 (or 2×3) that creates a pair of pixel-trios having triangular shape and offers a greater possible correlation among the pixels. This kernel thus can be better utilized for improving a PG-based RDH scheme. Considering this, we develop and present an improved PG-based RDH scheme and the computational models of its key processes. Experimental results demonstrated that our proposed RDH scheme offers reasonably better embedding rate-distortion performance than the original scheme.

Steganography hides the data within a media file in an imperceptible way. Steganalysis exposes steganography by using detection measures. Traditionally, Steganalysis revealed steganography by targeting perceptible and statistical properties which results in developing secure steganography schemes. In this work, we target LSB image steganography by using entropy and joint entropy metrics for steganalysis. First, the Embedded image is processed for feature extraction then analyzed by entropy and joint entropy with their corresponding original image. Second, SVM and Ensemble classifiers are trained according to the analysis results. The decision of classifiers discriminates cover image from stego image. This scheme is further applied on attacked stego image for checking detection reliability. Performance evaluation of proposed scheme is conducted over grayscale image datasets. We analyzed LSB embedded images by Comparing information gain from entropy and joint entropy metrics. Results conclude that entropy of the suspected image is more preserving than joint entropy. As before histogram attack, detection rate with entropy metric is 70% and 98% with joint entropy metric. However after an attack, entropy metric ends with 30% detection rate while joint entropy metric gives 93% detection rate. Therefore, joint entropy proves to be better steganalysis measure with 93% detection accuracy and less false alarms with varying hiding ratio.

The key element in runtime compression is the compression algorithm itself, that is used during processing. It has to be small in enough in decompression bytecode size to fit in the final executable, yet have to provide the best possible compression ratio. In our work we benchmark the top LZ based compression methods on Windows PE (both exe and dll) files, and present the results including the decompression overhead and the compression rates.

Call graphs provide basis for numerous interprocedural analysers and tools, therefore it is crucial how precisely they are constructed. Developers need to know the features of a call graph builder before applying it to subsequent algorithms. The characteristics of call graph builders are best understood by comparing the generated call graphs themselves. The comparison can be done by matching the corresponding nodes in each graph and then analysing the found methods and calls. In this paper, we developed a process for pairing the nodes of multiple call graphs produced for the same source code. As the six static analysers that we collected for call graph building handles Java language elements differently, it was necessary to refine the basic name-wise pairing mechanism in several steps. Two language elements, the anonymous and generic methods, needed extra consideration. We describe the steps of improvement and our final solution to achieve the best possible pairing through the analysis of the Apache Commons-Math project.

The networking dataplane is going through a paradigm shift as softwarization of switches sees an increased pull from the market. Yet, software tooling to support development with these new technologies is still in its infancy.In this work, we introduce a framework for verifying performance requirement conformance of data plane protocols defined in the P4 language . We present a framework that transforms a P4 program in a versatile symbolic formula which can be utilized to answer various performance queries. We represented the system using denotational semantics and it can be easily extended with low-level target-dependent information. We demonstrate the operation of this system on a toy specification.

Based on the available information in many cases it can happen that two objects cannot be distinguished. If a set of data is given and in this settwo objects have the same attribute values, then these two objects are called indiscernible. This indiscernibility has an effect on the membership relation,because in some cases it makes our judgment uncertain about a given object. The uncertainty appears because if something about an object is needed to bestated, then all the objects that are indiscernible from the given object must be taken into consideration. The indiscernibility relation is an equivalencerelation which represents background knowledge embedded in an information system. In a Pawlakian system this relation is used in set approximation.Correlation clustering is a clustering technique which generates a partition. In the authors’ previous research the possible usage of the correlation clusteringin rough set theory was investigated. In this paper the authors show how different types of search algorithms affect the set approximation.

The Internet of Things (IoT) paradigm is closely coupled with cloud technologies, and the support for managing sensor data is one of the primary concerns of Cloud Computing. IoT-Cloud systems are widely used to manage sensors and different smart devices connected to the cloud, hence a large amount of data is generated by these things that need to be efficiently stored and processed. Simulation platforms have the advantage of enabling the investigation of complex systems without the need of purchasing and installing physical resources. In our previous work, we chose the DISSECT-CF simulator to model IoT-Cloud systems, and we also introduced provider policies to enable cost-aware policies resource management. The aim of this paper is to further extend the simulation capabilities of this tool by enabling multi-cloud management. In this paper we introduce four cloud selection strategies aimed to reduce application execution time and utilization costs. We detail our proposed method towards multi-cloud extension, and evaluate defined strategies through a meteorological application.

In this work we investigate the effects of switching from mouse cursor control to gaze-based control in a computerized divided attention game. We conducted experiments with nine participants performing a task that requires continuous focused concentration and frequent shifts of attention. Despite carefully controlling experimental and design aspects, the performance of subjects was considerably impaired when using gaze-based control. The participants were experienced users of the mouse control version of the task, we adjusted the difficulty to the more demanding conditions and selected the parameters of gaze input based on previous research findings. In contrast to our assumptions, experienced users could not get used to gaze-based control in the amount of experiments we performed. Additionally we consider the strategies of users, i.e. their method of problem solving, and found that it is possible to make progress in our task even during a short amount of practice. The results of this study provide evidence that the adoption of interfaces controlled by human eye-gaze in cognitively demanding environments require careful design, proper testing and sufficient user training.