Branimir Pervan

Beat the Chasers is a popular UK-originating TV quiz show that premiered in Croatia in 2023. On the show, a contestant challenges a team of up to five chasers with respect to the offers provided by the production. Each offer balances risk and reward, varying in prize money, time advantage, and the number of chasers. In this paper, we first present the dataset obtained by extracting data from the publicly broadcast episodes of Beat the Chasers in Croatia. We then apply various machine-learning models with the goals of predicting (1) which offer a contestant is most likely to select and (2) the game’s outcome. The best-case results suggest that we can successfully do both by reaching an F1-score of 73.6% for the selected offer prediction and 84.6% for the game’s outcome prediction. Regarding the feature importance analysis, we identified the contestant’s hometown size, NUTS 2 region, age group, and gender as the most relevant features in the case of the selected offer prediction. As for the outcome prediction, the game-specific features emerged as the most important, namely, the cash builder result, the selected number of chasers, and the chasers’ time in the selected offer.

Despite the existing medical infrastructure being limited in terms of interoperability, the amount of medical multimedia transferred over the network and shared through various channels increases rapidly. In search of consultations with colleagues, medical professionals with the consent of their patients, usually exchange medical multimedia, mainly in the form of images, by using standard instant messaging services which utilize lossy compression algorithms. That consultation paradigm can easily lead to losses in image representation that can be misinterpreted and lead to the wrong diagnosis. This paper presents MIDOM—Medical Imaging and Diagnostics on the Move, a DICOM-based medical image communication system enhanced with a couple of variants of our previously developed custom lossless Classification and Blending Predictor Coder (CBPC) compression method. The system generally exploits the idea that end devices used by the general population and medical professionals alike are satisfactorily performant and energy-efficient, up to a point to support custom and complex compression methods successfully. The system has been implemented and appropriately integrated with Orthanc, a lightweight DICOM server, and a medical images storing PACS server. We benchmarked the system thoroughly with five real-world anonymized medical image sets in terms of compression ratios and latency reduction, aiming to simulate scenarios in which the availability of the medical services might be hardly reachable or in other ways limited. The results clearly show that our system enhanced with the compression methods in the question pays off in nearly every testing scenario by lowering the network latency to at least 60% of the latency required to send raw and uncompressed image sets and 25% in the best-case, while maintaining the perfect reconstruction of medical images and, thus, providing a more suitable environment for healthcare applications.

This paper presents the improved version of our cool Cracker cluster (cCc), a heterogeneous distributed system for parallel and energy-efficient bcrypt password hash computation. The cluster consists of up to 8 computational units (nodes) with different performances measured in bcrypt hash computations per second [H/s]. In the cluster, nodes are low-power heterogeneous embedded systems with programmable logic containing specialized hash computation accelerators. In the experiments, we used a combination of Xilinx Zynq-series SoC boards and ZTEX 1.15y board which was initially used as a bitcoin miner. Zynq based nodes use the improved version of our custom bcrypt accelerator, which executes the most costly parts of the bcrypt hash computation in programmable logic. The cluster was formed around the famous open-source password cracking software package John the Ripper (abbr. JtR). On the communication layer, we used Message Passing Interface (MPI)library with a standard Ethernet network connecting the nodes. To mitigate the different performances among the cluster nodes and to balance the load, we developed and implemented password candidate distribution scheme based on the passwords’ probability distribution, i.e. the order of appearance in the dictionary. We tested individual nodes and the cluster as a whole, trying different combinations of nodes and evaluating our distribution scheme for password candidates. We also compared our cluster with various GPU implementations in terms of performance, energy-efficiency, and price-efficiency. We show that our solution outperforms other platforms such as high-end GPUs, by a factor of at least 3 in terms of energy-efficiency and thus producing less overall cost of password attack than other platforms. In terms of the total operational costs, our cluster pays off after 4500 cracked passwords for a bcrypt hash with cost parameter 12, which makes it more appealing for real-world password-based system attacks. We also demonstrate the scalability of our cCc cluster.

In this paper, we present the Cool Cracker Cluster cCc: a heterogeneous distributed system for parallel, energy-efficient, and high-speed bcrypt password hash computation. The cluster consists of up to 32 heterogeneous nodes with Zynq-7000-based SoCs featuring a dual-core, general-purpose ARM processor coupled with FPGA programmable logic. Each node uses our custom bcrypt accelerator which executes the most costly parts of the hash computation in programmable logic. We integrated our bcrypt implementation into John the Ripper, an open source password cracking software. Message Passing interface (MPI) support in John the Ripper is used to form a distributed cluster. We tested the cluster, trying different configurations of boards (Zedboards and Pynq boards), salt randomness, and cost parameters finding out that password cracking scales linearly with the number of nodes. In terms of performance (number of computed hashes per second) and energy efficiency (performance per Watt), cCc outperforms current systems based on high-end GPU cards, namely Nvidia Tesla V100, by a factor of 2.72 and 5 respectively.