Rs4rs is a web application designed to perform semantic search on recent papers from top conferences and journals related to Recommender Systems. Current scholarly search engine tools like Google Scholar, Semantic Scholar, and ResearchGate often yield broad results that fail to target the most relevant high-quality publications. Moreover, manually visiting individual conference and journal websites is a time-consuming process that primarily supports only syntactic searches. Rs4rs addresses these issues by providing a user-friendly platform where researchers can input their topic of interest and receive a list of recent, relevant papers from top Recommender Systems venues. Utilizing semantic search techniques, Rs4rs ensures that the search results are not only precise and relevant but also comprehensive, capturing papers regardless of variations in wording. This tool significantly enhances research efficiency and accuracy, thereby benefitting the research community and public by facilitating access to high-quality, pertinent academic resources in the field of Recommender Systems. Rs4rs is available at https://rs4rs.com.

Fingerprinting codes based on the marking assumption provide reliable security against collusion attacks, but lack resilience against channel errors such as symbol erasures or other forms of distortions introduced by transmission over noisy or insecure communication channels. This additional source of distortion errors can be addressed by relaxing the marking assumption. In this paper we examine the restrictions and limitations on the code construction in terms of accusation errors, alphabet size, and the distortion errors under this weaker form of the marking assumption as formulated by Guth and Pfitzmann, also deriving a theoretical minimum lower bound on the code length of q-ary fingerprinting codes. We provide the formulas for applying the already existing binary Tardos code with symmetric accusation function under the weaker form of the marking assumption along with numerical results proving the tightness of the derived bounds. We show that the code is still of asymptotically minimum code length under the relaxation of the marking assumption.

We propose an algorithm that removes the visually unpleasant effects of cradling in X-ray images of panel paintings, with the goal of improving the X-ray image readability by art experts. The algorithm consists of three stages. In the first stage the location of the cradle is detected automatically and the grayscale inconsistency, caused by the thickness of the cradle, is corrected. In a second stage we use a method called morphological component analysis to separate the X-ray image into a so-called cartoon part and a texture part, where the latter contains mostly the wood grain from both the panel and the cradling. The algorithm next learns a Bayesian factor model that distinguishes between the texture patterns that originate from the cradle and those from other components such as the panel and/or the painting on the panel surface, and finally uses this to remove the textures associated with the cradle. We apply the algorithm to a number of historically important paintings on panel. We also show how it can be used to digitally remove stretcher artifacts from X-rays of paintings on canvas. We compare our results with those obtained manually by best current practices in art conservation as well as on a ground truth dataset, consisting of X-ray images of a painting before and after removal of the physically attached cradle.