Accurate detection and classification of breast cancer is a critical task in medical imaging due to the complexity of breast tissues. Due to automatic feature extraction ability, deep learning methods have been successfully applied in different areas, especially in the field of medical imaging. In this study, a novel patchbased deep learning method called Pa-DBN-BC is proposed to detect and classify breast cancer on histopathology images using the Deep Belief Network (DBN). Features are extracted through an unsupervised pre-training and supervised fine-tuning phase. The network automatically extracts features from image patches. Logistic regression is used to classify the patches from histopathology images. The features extracted from the patches are fed to the model as input and the model presents the result as a probability matrix as either a positive sample (cancer) or a negative sample (background). The proposed model is trained and tested on the whole slide histopathology image dataset having images from four different data cohorts and achieved an accuracy of 86%. Consequently, the proposed method is better than the traditional ones, as it automatically learns the best possible features and experimental results show that the model outperformed the previously proposed deep learning methods.

This paper presents a hybrid method for the construction of cryptographically strong bijective substitution-boxes by utilizing the merits of chaotic map and algebraic groups. The hybrid method first generates the key-dependent dynamic S-boxes using chaotic heuristic search strategy and then the S-boxes are evolved with the help of potent proposed algebraic group structures. This paper proposes a new improved combination chaotic map to operate initial search strategy. To augment the strength of generated S-boxes, the algebraic group structures are discovered which have the power to improve their cryptographic strength. The performance assessments using standard criterions are rendered to quantify the strengths of proposed bijective S-boxes. The experimental results and comparisons with recent S-box research findings justify the effectiveness and competence of the proposed bijective S-boxes and anticipated hybrid generation method.

With the continued increase of usage of High-Performance Computing (HPC) in scientific fields, the need for programming models in a heterogeneous architecture with less programming effort has become important in scientific applications. OpenACC is a high-level parallel programming model used with FORTRAN, C, and C++ programming languages to accelerate the programmers' code with fewer changes and less effort, which reduces programmer workloads and makes it easier to use and learn. Also, OpenACC has been increasingly used in many top supercomputers around the world, and three of the top five HPC applications in Intersect360 Research are currently using OpenACC. However, when programmers use OpenACC to parallelize their code without correctly understanding OpenACC directives and their usage or following OpenACC instructions, they can cause run-time errors that vary from causing wrong results, performance issues, and other undefined behaviors. In addition, building parallel systems by using a higher level programming model increase the possibility to introduce errors, and the parallel applications thus have non-determined behavior, which makes testing and detecting their run-time errors a challenging task. Although there are many testing tools that detect run-time errors, this is still inadequate for detecting errors that occur in applications implemented in high-level parallel programming models, especially OpenACC related applications. As a result, OpenACC errors that cannot be detected by compilers should be identified, and their causes should be explained. In this paper, our contribution is introducing new static techniques for detecting OpenACC errors, as well as for the first time classifying errors that can occur in OpenACC software programs. Finally, to the best of our knowledge, there is no published work to date that identifies or classifies OpenACC-related errors, nor is there a testing tool designed to test OpenACC applications and detect their run-time errors.INDEX TERMS OpenACC, OpenACC run-time errors, OpenACC error classifications, OpenACC testing tool, static approach for OpenACC application.

Cryptography is commonly used to secure communication and data transmission over insecure networks through the use of cryptosystems. A cryptosystem is a set of cryptographic algorithms offering security facilities for maintaining more cover-ups. A substitution-box (S-box) is the lone component in a cryptosystem that gives rise to a nonlinear mapping between inputs and outputs, thus providing confusion in data. An S-box that possesses high nonlinearity and low linear and differential probability is considered cryptographically secure. In this study, a new technique is presented to construct cryptographically strong 8×8 S-boxes by applying an adjacency matrix on the Galois field GF(28). The adjacency matrix is obtained corresponding to the coset diagram for the action of modular group PSL(2,Z) on a projective line PL(F7) over a finite field F7. The strength of the proposed S-boxes is examined by common S-box tests, which validate their cryptographic strength. Moreover, we use the majority logic criterion to establish an image encryption application for the proposed S-boxes. The encryption results reveal the robustness and effectiveness of the proposed S-box design in image encryption applications.

Earned Value Management (EVM) measures project performance against a baseline plan. It identifies deviations in budget and schedule, aids project managers in taking earlier corrective actions against cost and schedule overruns. Although the literature highlights the significance of scope by adopting it as a leading indicator to measure project success or failure. However, EVM does not include scope when evaluating the performance of any software project. While considering the importance of scope and its ever-changing nature, it is imperative to measure the effect of changes in scope on the project plan. To analyse such effects, this study aims to enhance the traditional EVM by incorporating scope into it. The main objectives of this paper are: i) to extract the effects of project scope changes, ii) to map extracted effects of project scope changes with Software Project Scope Rating Index (SPSRI) elements, and iii) to quantify the extracted effects and integrate them with EVM. An extensive literature review is conducted to achieve the first objective, which results in the seventeen unique effects; that were used to map with SPSRI elements. To forecast the variations in scope for a given project budget, Monte Carlo simulations were run on the top eight scope elements, whereas, the results were incorporated with EVM to identify the deviations between actual and projected values of scope's score and cost. Finally, the multivariate regression model was used to evaluate the influence of individual element on the overall estimated cost of the project. The correlation between the independent variables (SPSRI elements) and the dependent variable (overall cost) was calculated along with the valuation of each independent variable on the dependent variable. Moreover, the effects are statistically shown that independent variables have influenced the dependent variable. This technique could assist the project managers to forecast deviations in project scope earlier.