Robust image hashing seeks to transform a given input image into a shorter hashed version using a key-dependent non-invertible transform. These hashes find extensive applications in content authentication, image indexing for database search and watermarking. Modern robust hashing algorithms consist of feature extraction, a randomization stage to introduce non-invertibility, followed by quantization and binary encoding to produce a binary hash. This paper describes a novel algorithm for generating an image hash based on Log-Polar transform features. The Log-Polar transform is a part of the Fourier-Mellin transformation, often used in image recognition and registration techniques due to its invariant properties to geometric operations. First, we show that the proposed perceptual hash is resistant to content-preserving operations like compression, noise addition, moderate geometric and filtering. Second, we illustrate the discriminative capability of our hash in order to rapidly distinguish between two perceptually different images. Third, we study the security of our method for image authentication purposes. Finally, we show that the proposed hashing method can provide both excellent security and robustness.

The paper presents (1) a new library (API) that provides easy access to the Romanian WordNet and (2) an application based on the API to perform embedded vector retrofitting. The new API (further referred to as RoWn) is built in Python and offers direct access to all the data provided in the Romanian WordNet. It implements all basic I/O and query operations. As RoWn is based on a directed graph structure powered by the versatile networkx library, users are provided with the groundwork to implement powerful graph algorithms. The second part of the paper presents an application built on RoWn: the process of vector retrofitting. “Retrofitting” refers to the semantic specialization of word vectors, a process of fine-tuning each vector under a specified constraint. We use the synonymy/antonymy relations extracted from RoWn to tune the word vectors under these semantic constraints, effectively specializing the vector space.

Model-based testing is an approach capable of providing one of the highest automation degree for the testing process. But its success depends on the validity of the model. One of the factors that contributes to developing valid models is the specification language used for this task. This paper presents a work in progress regarding the proposal of a new domain specific language for ad hoc networks. Current specification languages are not ad hoc network aware, so they cannot express the special characteristics of these networks. As a consequence, the test cases generated from such models do not address ad hoc specific aspects and thus are prone to not find some errors. Our proposal is based on the Gamma formalism, an implementation of the chemical reaction model. We will show how using this approach enables our new language to express the needed properties for the ad hoc networks and to generate the targeted test cases.

Model checking has proved to be a very useful formal verification technique in the design of communication and security protocols. Researches have used it to validate the communications protocols from the point of view of their functional and security specifications. Over the years, model checking has evolved from explicit encoding of the state space to symbolic encoding, thus overcoming the state space explosion problem and being able to handle almost infinite state spaces. Research has indicated that another means of improving the symbolic encoding of the state space is to encode only the states of the system, but not the transition relations. But this raises the problem of how to compute the path to the counter-examples. This paper address the issue and proposes the use of a backward traversal of the state space. The idea is to start from the counter-example state and compute its predecessor(s), then take each of the predecessors and apply recursively the same inversing operation until the initial state is reached, thus obtaining the sought path. The new approach was formally defined and exemplified.

Relation extraction is an important task for structuring information from unstructured text. However, the Romanian language still lacks dedicated datasets and benchmarks for this task. To address this gap, we introduce RoRED, a Romanian relation extraction dataset built by combining two complementary data construction strategies: translating existing high-quality English resources and applying distant supervision to native Romanian Wikipedia data. We leverage a powerful open-source large language model to automatically translate English examples into Romanian. For the native subset, we align Romanian Wikipedia entities with Wikidata relations to obtain naturally occurring Romanian examples. To better reflect real-world relation extraction scenarios, we also introduce synthetic negative examples generated using existing Romanian named entity recognition models. Finally, we validate the dataset by fine-tuning and evaluating multiple baseline models. Our strongest model, LUKE-RoRED, achieves a macro-F1 score of $0.8744$ on the RoRED test set, demonstrating that the dataset can support relation extraction for Romanian. Overall, RoRED provides a strong first native benchmark for Romanian relation extraction.