Abstract
Advancements in semiconductor technology no longer occur at the pace the industry had been accustomed to. We have entered what is considered by many to be the post-Moore era. In order to continue scaling performance, increasingly heterogeneous architectures are being developed and the use of special purpose accelerators is on the rise. One notable example are Field-Programmable-Gate-Arrays (FPGAs), both in the data-center and embedded spaces. Advances in FPGA features and tools is allowing for critical kernels to be accelerated on specialized hardware without fabrication costs. However, re-targeting code to such heterogeneous platforms still requires significant refactoring of the compute intensive kernels, as well as knowledge of parallel compute and hardware design concepts for maximization of performance. We present Tribble, a source-to-source framework under active development, capable of transforming regular C/C++ programs for execution on heterogeneous architectures. This includes transforming the target kernel source code so that it is amenable for circuit generation while keeping the original version for software execution, inserting code for task and memory management and injecting a scheduler algorithm.

Abstract

Abstract
Over the years, many solutions have been suggested in order to improve object detection in maritime environments. However, none of these approaches uses flight information, such as altitude, camera angle, time of the day, and atmospheric conditions, to improve detection accuracy and network robustness, even though this information is often available and captured by the UAV. This work aims to develop a network unaffected by image-capturing conditions, such as altitude and angle. To achieve this, metadata was integrated into the neural network, and an adversarial learning training approach was employed. This was built on top of the YOLOv7, which is a state-of-the-art realtime object detector. To evaluate the effectiveness of this methodology, comprehensive experiments and analyses were conducted. Findings reveal that the improvements achieved by this approach are minimal when trying to create networks that generalize more across these specific domains. The YOLOv7 mosaic augmentation was identified as one potential responsible for this minimal impact because it also enhances the model's ability to become invariant to these image-capturing conditions. Another potential cause is the fact that the domains considered (altitude and angle) are not orthogonal with respect to their impact on captured images. Further experiments should be conducted using datasets that offer more diverse metadata, such as adverse weather and sea conditions, which may be more representative of real maritime surveillance conditions. The source code of this work is publicly available at https://git hub.com/ipleiria-robotics/maritime-metadata-adaptation.

Abstract

Abstract

Abstract
Energy efficiency is a non-functional requirement that developers must consider, particularly when building software for battery-operated devices like mobile ones: a long-lasting battery is an essential requirement for an enjoyable user experience.In previous studies, it has been shown that many mobile applications include inefficiencies that cause battery to be drained faster than necessary. Some of these inefficiencies result from software patterns that have been catalogued, and for which more energy-efficient alternatives are also known.The existing catalogues, however, assume as a fundamental requirement that one has access to the source code of an application in order to be able to analyse it. This requirement makes independent energy analysis challenging, or even impossible, e.g. for a mobile user or, most significantly, an App Store trying to provide information on how efficient an application being submitted for publication is.We study the viability of looking for known energy patterns in applications by decompiling them and analysing the resulting code. For this, we decompiled and analysed 420 open-source applications by extending an existing tool, which is now capable of transparently decompiling and analysing android applications. With the collected data, we performed a comparative study of the presence of four energy patterns between the source code and the decompiled code.We performed two types of analysis: (i) comparing the total number of energy pattern detections; (ii) comparing the similarity between energy pattern detections. When comparing the total number of detections in source code against decompiled code, we found that 79.29% of the applications reported the same number of energy pattern detections.To test the similarity between source code and APKs, we calculated, for each application, a similarity score based on our four implemented detectors. Of all applications, 35.76% achieved a perfect similarity score of 4, and 89.40% got a score of 3 or more out of 4. Furthermore, only two applications got a score of 0.When viewed in tandem, the results of the two analyses we performed point in a promising direction. They provide initial evidence that static analysis techniques, typically used in source code, can be a viable method to inspect APKs when access to source code is restricted, and further research in this area is worthwhile.