Abstract
This paper presents SpecRF-Posture, a novel low-cost approach for accurate Human Posture Recognition (HPR) using Radio Frequency (RF) signals. SpecRF-Posture leverages S21 parameters within the WiFi-6E frequency range for classification. We obtain a dataset of S21 parameters for different postures by performing beamscanning through mechanical rotation of a horn transmitter aimed at a reflective surface that illuminates the space of interest. We determine the S21 parameters of the signals that are then reflected back from the space onto an omni-directional receiver. Thus for each posture we attain the S21 parameters of each possible illumination direction of the space. Experimental results demonstrate that SpecRF-Posture achieves an accuracy of 99.17% in posture classification, highlighting its effectiveness. Additionally, an RF dataset was acquired using a software package for automatic data acquisition within the WiFi-6E frequency range, and both the dataset and the software package have been made publicly available.

Abstract
Background: Many progranunmg environments include automated feedback in the form of hints to help novices learn autonomously. Some experimental studies investigated the impact of automated liints in the immediate, performance and learning retention in that context. Automated feedback is also becoming a popular research topic in the context of formal specification languages, but so far no experimental studies have been conducted to assess its impact while learning such languages. Objective: We aim to investigate the impact of different types of automated hints while learning a formal specification language, not only in terms of immediate performance and learning retention, but also in the emotional response of the students. Method: We conducted a simple one-factor randomised experiment in 2 sessions involving 85 BSc students majoring in CSE. In the 1st session students were divided in 1 control group and 3 experimental groups, each receiving a different type of hint while learning to specify simple, requirements with the Alloy formal specification language. To assess the impact of hints on learning retention, in the 2nd session, 1 week later, students had no hints while formalising requirements. Before and after each session the students answered a standard self-reporting emotional survey to assess their emotional response to the experiment. Results: Of the 3 types of hints considered, only those pointing to the precise location of an error had a positive impact on the immediate performance and none had significant impact in learning retention. Hint availability also causes a significant impact on the emotional response, but no significant emotional :impact exists once hints are no longer available (i.e. no deprivation effects were detected). Conclusion: Although none of the evaluated hints had an impact on learning retention, learning a formal specification language with an environment that provides hints with precise error locations seems to contribute to a better overall experience without apparent drawbacks. Further studies are needed to investigate if other kind of feedback, namely hints combined with some sort of self explanation prompts, can have a positive impact in learning retention.

Abstract
Professional training presents a significant challenge for organizations, particularly in captivating and engaging employees in these learning initiatives. With the ever-evolving landscape of workplace education, various learning modes have emerged within organizations, and e-learning stands out as a prominent choice. This increasingly cost-effective and adaptable solution has revolutionized training by facilitating numerous learning activities, including the seamless integration of educational games driven by cutting-edge technologies. However, incorporating serious games into educational and professional settings introduces its own set of challenges, particularly in quantifying their tangible impact on learning and assessing their adaptability across diverse contexts. Organizations require a consistent framework to guide best practices in implementing e-learning combined with serious games in professional training. The primary objective of this research is to bridge this gap. Rooted in the methodology of Design Science Research, it aims to provide a comprehensive framework for creating and assessing adaptive serious games that achieve desired learning and engagement outcomes. The overarching goal is to enhance the teaching-learning process in professional training, ultimately elevating student engagement and boosting learning outcomes to new heights. The proposal is grounded in a review of literature, expert insights, and user experiences with Serious Games in professional training, considering learning outcomes and forms of adaptation as essential characteristics for developing or evaluating Serious Games. The result is a framework designed to guide learners toward improved learning outcomes and increased engagement. The proposal underwent evaluation through triangulation, involving focus groups and expert interviews. Additionally, it was utilized in the development and assessment of a Serious Game, offering new insights and application suggestions. This experiment provided an evaluation of the framework based on real courses. In summary, this investigation contributes to the development of evidence-based approaches for the effective use of Serious Games in professional training.

Abstract
Biofouling in marine optical sensors poses a significant challenge as it can compromise data accuracy and instrument functionality. This study investigates the effectiveness of local chlorine generation by seawater electrolysis in mitigating biological fouling and extending the operational lifespan of optical oceanographic instruments. Eight similar turbidity probes integrated with a local chlorine generation system, along with a turbidity probe constructed from ABS and another from PLA with copper filament, were developed for testing in the marine environment. The chlorine probes were designed into two groups: four utilizing standard FTO glass and four featuring FTO glass coated with platinum nanoparticles. Each set of probes employed different excitation currents for chlorine generation. All probes underwent laboratory calibration using formazine before deployment in a coastal environment for 97 days. The findings demonstrate a correlation with higher electrical power leading to prolonged operation intervals free from biofouling interference. Additionally, probes coated with platinum nanoparticles demonstrate higher performance in comparison to those with standard FTO glass. The copper probe did not effectively shield the optical transducers from microfouling, although it effectively demonstrated its efficacy in protecting the structural housing of the device. Overall, this work offers a compelling in situ demonstration of local chlorine generation as a promising strategy for enhancing the performance and longevity of optical oceanographic instruments in marine environments.

Abstract
We introduce a novel method for movie genre classification, capitalizing on a diverse set of readily accessible pretrained models. These models extract high-level features related to visual scenery, objects, characters, text, speech, music, and audio effects. To intelligently fuse these pretrained features, we train small classifier models with low time and memory requirements. Employing the transformer model, our approach utilizes all video and audio frames of movie trailers without performing any temporal pooling, efficiently exploiting the correspondence between all elements, as opposed to the fixed and low number of frames typically used by traditional methods. Our approach fuses features originating from different tasks and modalities, with different dimensionalities, different temporal lengths, and complex dependencies as opposed to current approaches. Our method outperforms state-of-the-art movie genre classification models in terms of precision, recall, and mean average precision (mAP). To foster future research, we make the pretrained features for the entire MovieNet dataset, along with our genre classification code and the trained models, publicly available.

Abstract
Secure multiparty computation (SMC) provides collaboration among multiple parties, ensuring the confidentiality of their private information. However, classical SMC implementations encounter significant security and efficiency challenges. Resorting to the entangled Greenberger-Horne-Zeilinger (GHZ) state, we propose a quantum-based two-party protocol to compute binary Boolean functions, with the help of a third party. We exploit a technique in which a random Z-phase rotation on the GHZ state is performed to achieve higher security. The security and complexity analyses demonstrate the feasibility and improved security of our scheme compared to other SMC Boolean function computation methods. Additionally, we implemented the proposed protocol on the IBM QisKit and found consistent outcomes that validate the protocol's correctness.