TWGH: A Tripartite Whale–Gray Wolf–Harmony Algorithm to Minimize Combinatorial Test Suite Problem

Today’s academics have a major hurdle in solving combinatorial problems in the actual world. It is nevertheless possible to use optimization techniques to find, design, and solve a genuine optimal solution to a particular problem, despite the limitations of the applied approach. A surge in interest in population-based optimization methodologies has spawned a plethora of new and improved approaches to a wide range of engineering problems. Optimizing test suites is a combinatorial testing challenge that has been demonstrated to be an extremely difficult combinatorial optimization limitation of the research. The authors have proposed an almost infallible method for selecting combinatorial test cases. It uses a hybrid whale–gray wolf optimization algorithm in conjunction with harmony search techniques. Test suite size was significantly reduced using the proposed approach, as shown by the analysis of the results. In order to assess the quality, speed, and scalability of TWGH, experiments were carried out on a set of well-known benchmarks. It was shown in tests that the proposed strategy has a good overall strong reputation test reduction size and could be used to improve performance. Compared with well-known optimization-based strategies, TWGH gives competitive results and supports high combinations (2 ≤ t ≤ 12)

TWGH: A Tripartite Whale–Gray Wolf–Harmony Algorithm to Minimize Combinatorial Test Suite Problem

Today’s academics have a major hurdle in solving combinatorial problems in the actual world. It is nevertheless possible to use optimization techniques to find, design, and solve a genuine optimal solution to a particular problem, despite the limitations of the applied approach. A surge in interest in population-based optimization methodologies has spawned a plethora of new and improved approaches to a wide range of engineering problems. Optimizing test suites is a combinatorial testing challenge that has been demonstrated to be an extremely difficult combinatorial optimization limitation of the research. The authors have proposed an almost infallible method for selecting combinatorial test cases. It uses a hybrid whale–gray wolf optimization algorithm in conjunction with harmony search techniques. Test suite size was significantly reduced using the proposed approach, as shown by the analysis of the results. In order to assess the quality, speed, and scalability of TWGH, experiments were carried out on a set of well-known benchmarks. It was shown in tests that the proposed strategy has a good overall strong reputation test reduction size and could be used to improve performance. Compared with well-known optimization-based strategies, TWGH gives competitive results and supports high combinations (2 ≤ t ≤ 12)

Parallelizing RSA Algorithm on Multicore CPU and GPU

Public key algorithms are extensively known to be slower than symmetric key alternatives in the a r e a of cryptographic algorithms for the reason of their basis in modular arithmetic. The most public key algorithm widely used is the RSA. Therefore, how to enhance the speed of RSA algorithm has been the research significant topic in the computer security as well as in computing fields. With remarkable increase in the computing capability of the modern Graphics Processing Unit's (GPUs) as a co-processor of the CPU, one can significantly benefit from the Single Instruction Multiple Thread (SIMT) style of computing. This paper proposes a hybrid system to parallelize the RSA for multicore CPU and many cores GPUs with variable key size. In doing so, three variants implementation for the RSA algorithm are done to facilitate the performance comparison against Crypto++ library and sequential counterpart. The GPU implementation gained approximately 23 speed up factor over the sequential CPU implementation; while the multithread CPU implementation gained only 6 speed up factor over the sequential CPU implementation as far as the latency is concerned. Furthermore, additional speedup could be gained as far as the throughput is concerned; the throughput gained for 1024 bits is ~1800 msg/sec; as for 2048 bits is ~250 msg/sec. Due to overlapping of multithread operation whenever free resources are available. The experiments are conducted on a laptop with Intel Core I7-2670QM, 2.20 GHz CPU and Nvidia GeForce GT630M GPU. Results reveal that the GPU is appropriate to speed up the RSA algorithm

An open cloud-based platform for the creation and delivery of smart applications and services

With the increasing integration of computers and smartphones into our daily lives, in addition to the numerous benefits it offers over traditional paper-based methods of conducting affairs, it has become necessary to incorporate one of the most essential facilities into this integration; namely: colleges. The traditional approach for conducting affairs in colleges is mostly paper-based, which only increases time and workload and is relatively decentralized. This project provides educational and management services for the university environment, targeting the staff, the student body, and the lecturers, on two of the most used platforms: smartphones and web applications. The services include project management, attendance marking, various notifications and alerts, files and resources, a grading system, and an assignment management system. This project also aims to ultimately digitalize most of the information inside the college. The web platform contains accounts for an admin; who is the person with the highest authority within the system, the head of the department, the department coordinator, the project coordinator, the lecturers, and the students. Each of those accounts has its privileges and the interaction between those accounts is managed via a backend system. The mobile platform targets the students exclusively and is linked with the web platform