Current Issue : April - June Volume : 2021 Issue Number : 2 Articles : 5 Articles
Structural modeling is an important branch of software reliability modeling. It works in the early reliability engineering to optimize the architecture design and guide the later testing. Compared with traditional models using test data, structural models are often difficult to be applied due to lack of actual data. A software metrics-based method is presented here for empirical studies. The recurrent neural network (RNN) is used to process the metric data to identify defeat-prone code blocks, and a specified aggregation scheme is used to calculate the module reliability. Based on this, a framework is proposed to evaluate overall reliability for actual projects, in which algebraic tools are introduced to build the structural reliability model automatically and accurately. Studies in two open-source projects show that early evaluation results based on this framework are effective and the related methods have good applicability....
Nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data compression and its capability of extracting highly interpretable parts from data sets, and it has also been applied to various fields, such as recommendations, image analysis, and text clustering. However, as the size of the matrix increases, the processing speed of nonnegative matrix factorization is very slow. To solve this problem, this paper proposes a parallel algorithm based on GPU for NMF in Spark platform, which makes full use of the advantages of in-memory computation mode and GPU acceleration. The new GPU-accelerated NMF on Spark platform is evaluated in a 4-node Spark heterogeneous cluster using Google Compute Engine by configuring each node a NVIDIA K80 CUDA device, and experimental results indicate that it is competitive in terms of computational time against the existing solutions on a variety of matrix orders. Furthermore, a GPU-accelerated NMF-based parallel collaborative filtering (CF) algorithm is also proposed, utilizing the advantages of data dimensionality reduction and feature extraction of NMF, as well as the multicore parallel computing mode of CUDA. Using real MovieLens data sets, experimental results have shown that the parallelization of NMF-based collaborative filtering on Spark platform effectively outperforms traditional user-based and item-based CF with a higher processing speed and higher recommendation accuracy....
How to effectively resist synchronization attacks is the most challenging topic in the research of robust watermarking algorithms. A robust and blind audio watermarking algorithm for overcoming synchronization attacks is proposed in dual domain by considering time domain and transform domain. Based on analysing the characteristics of synchronization attacks, an implicit synchronization mechanism (ISM) is developed in the time domain, which can effectively track the appropriate region for embedding and extracting watermarks. The data in this region will be subjected to discrete cosine transform (DCT) and singular value decomposition (SVD) in turn to obtain the eigenvalue that can be utilized to carry watermarks. In order to extract the watermark blindly, the eigenvalue will be quantized. Genetic algorithm (GA) is utilized to optimize the quantization step to balance both transparency and robustness. The experimental results confirm that the proposed algorithm not only withstands various conventional signal processing operations but also resists malicious synchronization attacks, such as time scale modification (TSM), pitch-shifting modification (PSM), jittering, and random cropping. Especially, it can overcome TSM with strength from −30% to +30%, which is much higher than the standard of the International Federation of the Phonographic Industry (IFPI) and far superior to the other algorithms in related papers....
System products are widely used in almost all applications. Most of the human capacity has been converted to software solutions. Measuring and evaluating the quality of software products has become a problem for many companies that may be looking for software solutions. There are a number of skills that are required and used to make good software. As time goes on, new software is advancing and this has caused security problems to persist and thus affect the software’s performance. New components have introduced a new security system, which is called software security enhancement. In this study, I found the latest methods and techniques used to detect a few software errors and all other security threats. This method has the potential to identify possible symptoms indicating these causes. The new method has the ability to detect weaknesses before an attack. The VDC, which modifies the structure, compares its resources, and then gives a public account of those attacks. This method is used to remove the best security measures used to convince users and developers of the same model that will be used to crack down on software attacks. This paper presents a description of security objectives and best algorithms to address vulnerability issues to provide better results from planned attacks. The article deals with the implementation of the technical program. Finally, an analysis of the results was conducted to demonstrate the performance of this approach to the development of more secure systems....
Hard Lattice problems are assumed to be one of the most promising problems for generating cryptosystems that are secure in quantum computing. The shortest vector problem (SVP) is one of the most famous lattice problems. In this paper, we present three improvements on GPU-based parallel algorithms for solving SVP using the classical enumeration and pruned enumeration. There are two improvements for preprocessing: we use a combination of randomization and the Gaussian heuristic to expect a better basis that leads rapidly to a shortest vector and we expect the level on which the exchanging data between CPU and GPU is optimized. In the third improvement, we improve GPU-based implementation by generating some points in GPU rather than in CPU. We used NVIDIA GeForce GPUs of type GTX 1060 6G. We achieved a significant improvement upon Hermans’s improvement. The improvements speed up the pruned enumeration by a factor of almost 2.5 using a single GPU. Additionally, we provided an implementation for multi-GPUs by using two GPUs. The results showed that our algorithm of enumeration is scalable since the speedups achieved using two GPUs are almost faster than Hermans’s improvement by a factor of almost 5. The improvements also provided a high speedup for the classical enumeration. The speedup achieved using our improvements and two GPUs on a challenge of dimension 60 is almost faster by factor 2 than Correia’s parallel implementation using a dual-socket machine with 16 physical cores and simultaneous multithreading technology....
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