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Computer Vision (CV) has become an essential field in Artificial Intelligence applications. Object detection and recognition (ODR) is one of the fundamental tasks of computer vision implementations. However, developing an efficient ODR model is still a significant problem. The model's execution time and speed are the most critical features during the inference or detection and recognition process, which need to be improved using the latest object detection architectures. In this paper, the handwritten detection and recognition (HDR) model is developed based on previously known algorithms with their efficiency, such as Faster R-CNN and YOLOv4 in the first hand. On the other hand, two new models capable of detecting and recognizing handwritten digits using the latest ODR algorithm are proposed, one based on the latest YOLO family architecture (YOLOv5-HDR) with high speed and accuracy and the other using the transformers architecture (DETR). To the best of our knowledge, this is the first study to achieve a details comparison between YOLOv5 and transformers-based models in handwritten digit detection. Finally, the detailed performance analysis achieved by the paper proves that the YOLOv4-based model achieved the testing inference 13% faster than Faster R-CNN. However, the proposed YOLOv5-based model outperformed the YOLOv4 and the transformers-based one as it increased the testing execution time 25% faster than the YOLOv4, three times faster than the DETR model. A further adversarial attack test has been conducted to ensure the robust performance of the proposed model. Furthermore, numerical experiment results and their analyses demonstrate the robustness and effectiveness of the proposed YOLOv5-based model being the most stable for handwritten digit detection and recognition tasks. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm that combines symbolic execution and data flow testing to generate test cases satisfying multiple coverage criteria of critical software applications. The coverage criteria considered are data flow coverage as the primary criterion, software safety requirements, and equivalence partitioning as sub-criteria. black The characteristics of the subjects used for the study include high-precision floating-point computation and iterative programs. The work proposes an algorithm that aids the tester in automated test data generation, satisfying multiple coverage criteria for critical software. The algorithm adapts itself and selects different heuristics based on program characteristics. The algorithm has an intelligent agent as its decision support system to accomplish this adaptability. Intelligent agent uses the knowledge base to select different low-level heuristics based on the current state of the problem instance during each generation of genetic algorithm execution. The knowledge base mimics the expert's decision in choosing the appropriate heuristics. black The algorithm outperforms by accomplishing 100% data flow coverage for all subjects. In contrast, the simple genetic algorithm, random testing and a hyper-heuristic algorithm could accomplish a maximum of 83%, 67% and 76.7%, respectively, for the subject program with high complexity. black The proposed algorithm covers other criteria, namely equivalence partition coverage and software safety requirements, with fewer iterations. black The results reveal that test cases generated by the proposed algorithm are also effective in fault detection, with 87.2% of mutants killed when compared to a maximum of 76.4% of mutants killed for the complex subject with test cases of other methods. [ABSTRACT FROM AUTHOR]

There has been a lot of research on edge-computing task offloading in deep reinforcement learning (DRL). Deep reinforcement learning is one of the important algorithms in the current AI field, but there is still room for improvement in the time cost and adaptive correction ability of the algorithm. This paper studies the application of DRL algorithms in edge-computing task offloading, and its key innovation is to propose an MADRLCO algorithm, which is based on the design idea of the Actor–Critic framework, uses the DNN model to act as an Actor, and can more accurately locate the initial decision through iterative training, and use the LSTM model to optimize the Critic, which can be more accurate. The optimal decision can be located in a short period of time. The main work of this paper is divided into three parts: (1) The AC algorithm of the Actor–Critic framework in DRL is proposed to be applied to edge-computing task offloading. (2) To address the weak generalization ability of the basic version of the Actor–Critic algorithm in multi-objective optimization, the sequential quantitative correction and adaptive correction parameter K method are used to optimize the Critic frame, thereby improving the generalization ability of the model in multi-objective decision-making and improving the rationality of decision-making results. (3) Aiming at the problem of large time cost in the critical framework of the model, a search algorithm for resource allocation-related parameters based on the time-series prediction method is proposed (time-series forecasting is a research branch of pattern recognition), which reduces the time overhead of the algorithm and improves the adaptive correction capability of the model. The algorithm in this paper can adapt to not only the time-varying network channel state, but also the time-varying number of device connections. Finally, it is proved by experiments that compared with the DRL calculation offloading algorithm based on DNN plus binary search, the MADRLCO algorithm reduces the model training time by 66.27%, and in the environment of the time-varying number of devices in the metasystem, the average model average. The standard calculation rate is 0.0403 higher than that of the current optimal algorithm. [ABSTRACT FROM AUTHOR]