Abstract
Artificial intelligence is gaining attraction in more ways than ever before. The popularity of language models and AI-based businesses has soared since ChatGPT was made available to the public via the OpenAI web platform. It gains popularity in a very short period because of its real-world problem-solving capability. Considering the widespread use of ChatGPT and the people relying on it, this study determined how reliable ChatGPT can be used for learning in the medical domain. The capability of ChatGPT was evaluated using the questions of Harvard University gross anatomy and the United States Medical Licensing Examination (USMLE). The outcome of the ChatGPT was analyzed using a 2-way ANOVA and post-hoc analysis. Both tests showed systematic covariation between format and prompt. Furthermore, the physician adjudicators independently rated the outcome's accuracy, concordance, and insight into the answers given by ChatGPT. As a result of the analysis, ChatGPT-generated answers were more context-oriented and represented a better model for deductive reasoning than regular Google search results. Furthermore, ChatGPT obtained 58.8% on logical questions and 60% on ethical questions. This means that the ChatGPT is approaching the passing range for logical questions and has crossed the threshold for ethical questions. These results indicate that ChatGPT and other language-learning models can be invaluable tools for e-learners.

Abstract
Wi-Fi 6E will enable dense communications with low latency and high throughput, meeting the demands of ever growing network traffic and supporting emergent services such as ultra HD or multi-video streaming, and augmented or virtual reality. However, the 6GHz band suffers from higher path loss and signal attenuation, and poor performance in NLoS conditions. Reconfigurable Intelligent Surfaces (RISs) can address these challenges by providing low-cost directional communications with increased spectral and energy efficiency. However, RIS designs for the WiFi-6E range are under-explored in literature. We present the implementation of an 8x8 RIS tuned for 6.5GHz designed for scalability. We characterize the response of the unit cell, and evaluate the RIS in an anechoic chamber, measuring the far field radiation patterns for several digital beamsteering configurations in a horizontal plane, demonstrating effective signal steering.

Abstract
TCP is the default transport protocol of choice, namely for message-oriented middleware protocols (e.g., ZMTP, AMQP, MQTT) or distributed language runtimes (e.g., distributed Erlang), where exactly-once (EO) messaging is paramount. However, EO is only guaranteed within the TCP session, since reality shows that TCP connections can fail under many circumstances. Ensuring EO delivery ends up at the middleware layer, at the cost of higher complexity and lack of obliviouness - due to the use of permanent per-peer state. Moreover, using TCP at scale in highly concurrent systems leads to the need for TCP connection multiplexing, and possibly drastic performance loss due to head-of-line blocking. This paper introduces Exon, an oblivious exactly-once messaging protocol, and a corresponding lightweight (requiring no persistent storage, minimal memory, and low computation) library implementation over UDP. Exon uses a novel strategy of a per-message four-way protocol to ensure oblivious exactly-once messaging, with on-demand protocol-level "soft half-connections", established when needed and safely discarded. Obliviousness here refers to the protocol's ability to discard connection-specific state between incarnations, although some global information is retained. Exon achieves simultaneously: correctness with no timing assumptions, obliviousness, and performance through merging and pipelining basic protocol messages. Exon also employs a reliable delegation technique to handover the sending responsibility to a mediating node, without violating EO, when the sender the receiver are directly unreachable to each other and even if the message had already been delivered. The empirical evaluation of Exon demonstrates significant improvements (40%) over TCP in throughput and latency under packet loss, while maintaining a negligible (8%) overhead in healthy networks.

Abstract
General-purpose AI-assisted tools, such as ChatGPT, have recently gained much attention from the media and the general public. That raised questions about in which tasks we can apply such a tool. A good code design is essential for agile software development to keep it ready for change. In this context, identifying which design pattern can be appropriate for a given scenario can be considered an advanced skill that requires a high degree of abstraction and a good knowledge of object orientation. This paper aims to perform an exploratory study investigating the effectiveness of an AI-assisted tool in assisting developers in choosing a design pattern to solve design scenarios. To reach this goal, we gathered 56 existing questions used by teachers and public tenders that provide a concrete context and ask which design pattern would be suitable. We submitted these questions to ChatGPT and analyzed the answers. We found that 93% of the questions were answered correctly with a good level of detail, demonstrating the potential of such a tool as a valuable resource to help developers to apply design patterns and make design decisions.

Abstract
Recent state-of-the-art forecasting methods are trained on collections of time series. These methods, often referred to as global models, can capture common patterns in different time series to improve their generalization performance. However, they require large amounts of data that might not be available. Moreover, global models may fail to capture relevant patterns unique to a particular time series. In these cases, data augmentation can be useful to increase the sample size of time series datasets. The main contribution of this work is a novel method for generating univariate time series synthetic samples. Our approach stems from the insight that the observations concerning a particular time series of interest represent only a small fraction of all observations. In this context, we frame the problem of training a forecasting model as an imbalanced learning task. Oversampling strategies are popular approaches used to handle the imbalance problem in machine learning. We use these techniques to create synthetic time series observations and improve the accuracy of forecasting models. We carried out experiments using 7 different databases that contain a total of 5502 univariate time series. We found that the proposed solution outperforms both a global and a local model, thus providing a better trade-off between these two approaches.

Abstract
The SEAGUARD concept addresses a multi-domain (air, sea, underwater) maritime surveillance approach, involving the deployment, management and coordination of heterogeneous platforms, sensors and information technologies. SEAGUARD’s aim is to deliver a high level of situational awareness through a holistic surveillance system fitted to the needs and ambition of modern border management authorities. The operational context of large maritime areas and the nature of threats - increasingly dynamic, transnational and highly mobile - reflect the growing need to have multiple and different types of authorities involved in and coordinating response efforts so that, working together, their common goals are achieved, with superior efficiency and effectiveness. Attaining the SEAGUARD vision requires a high level of interoperability between the diverse and heterogeneous participating entities (organizations, units, people) and technological systems (unmanned platforms and smart devices) in a collective working in a civil- military, cross-organisation and cross-border environment. To enable this advanced synchronization, the SEAGUARD Interoperability Framework (S.IF) implements a set of Command and Control (C2) rules and protocols among participating entities, benefitting from NATO C2 Approaches as foundational references for its novel interoperability approach.