The accurate labeling of species and size of specimens plays a pivotal role in fish auctions conducted at fishing ports. These labels, among other relevant information, serve as determinants of the objectivity of the auction preparation process, underscoring the indispensable nature of a reliable labeling system. Historically, this task has relied on manual processes, rendering it vulnerable to subjective interpretations by the involved personnel, therefore compromising the value of the merchandise. Consequently, the digitization and implementation of an automated labeling system are proposed as a viable solution to this ongoing challenge. This study presents an automatic system for labeling species and size, leveraging pre-trained convolutional neural networks. Specifically, the performance of VGG16, EfficientNetV2L, Xception, and ResNet152V2 networks is thoroughly examined, incorporating data augmentation techniques and fine-tuning strategies. The experimental findings demonstrate that for species classification, the EfficientNetV2L network excels as the most proficient model, achieving an average F-Score of 0.932 in its automatic mode and an average F-Score of 0.976 in its semi-automatic mode. Concerning size classification, a semi-automatic model is introduced, where the Xception network emerges as the superior model, achieving an average F-Score of 0.949.

With the advent of the cloud computing model allowing a shared access to massive computing facilities, a surging demand emerges for the protection of the intellectual property tied to the programs executed on these uncontrolled systems. If novel paradigm as confidential computing aims at protecting the data manipulated during the execution, obfuscating techniques (in particular at the source code level) remain a popular solution to conceal the purpose of a program or its logic without altering its functionality, thus preventing reverse-engineering on the program even with the help of computing resources. The many advantages of code obfuscation, together with its low cost, makes it a popular technique. This paper proposes a novel methodology for source code obfuscation that can be used together with other traditional obfuscation techniques, making the code more robust against reverse engineering attacks. Three program complexity metrics are used to define three different single-objective combinatorial optimization versions of the problem, which are solved and analysed. Additionally, three multi-objective problems are defined, those considering each of the selected metrics together with the program execution time, in order to avoid strong obfuscations penalizing the performance. The goal of the defined problems is to find sequences of LLVM optimizations that lead to highly obfuscated versions of the original code. These transformations are applied to the back-end pseudo-assembly code (i.e., LLVM Intermediate Representation), thus avoiding any further optimizations by the compiler. Classical genetic algorithms (GAs) are used to solve the studied problems, namely a basic cellular GA for the single-objective problems and the popular NSGA-II for the multi-objective ones. The promising results show the potential of the proposed technique.