The integration of technology in education has become indispensable in acquiring new skills, knowledge, and competencies. This paper addresses the issue of analyzing and predicting the learning behavior of Computer Science students. Specifically, we present a dataset of compiler errors made by students during the first semester of an Introduction to Programming course where they learn the C programming language. We approach the problem of predicting the number of student errors as a missing data imputation problem, utilizing several prediction methods including Singular Value Decomposition, Polynomial Regression via Latent Tensor Reconstruction, Neural Network-based method, and Gradient Boosting. Our experimental results demonstrate high accuracy in predicting student learning behaviors over time, which can be leveraged to enhance personalized learning for individual students.

The ATLAS experiment relies on real-time hadronic jet reconstruction and b-tagging to record fully hadronic events containing b-jets. These algorithms require track reconstruction, which is computationally expensive and could overwhelm the high-level-trigger farm, even at the reduced event rate that passes the ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAS has mitigated these computational demands by introducing a fast neural-network-based b-tagger, which acts as a low-precision filter using input from hadronic jets and tracks. It runs after a hardware trigger and before the remaining high-level-trigger reconstruction. This design relies on the negligible cost of neural-network inference as compared to track reconstruction, and the cost reduction from limiting tracking to specific regions of the detector. In the case of Standard Model HH → bb̅bb̅, a key signature relying on b-jet triggers, the filter lowers the input rate to the remaining high-level trigger by a factor of five at the small cost of reducing the overall signal efficiency by roughly 2%.

Nationalism as an idea, movement and ideology denotes tendency of members of an ethnic group towards the establishment of an ethnically pure territory. Achieving this political goal, with superiority in relation to others as a guiding idea, often leads to jeopardizing sovereignty and territorial integrity and oppression and exploitation of other people. Nationalism has been a significant feature of the Russian society for centuries. Traditionally, the focus of Russian nationalism has been the preservation and strengthening of a large and powerful multi-ethnic state. Opposed to this imperial nationalism stands ethno-nationalism focused on the struggle for the interests of ethnic Russians. One of the main drivers of the Russian nationalism in the past couple of decades has been the country’s economic situation and the political tensions with the West. The Russian nationalist sentiment grew stronger after the annexation of Crimea and the beginning of war in Ukraine, being justified with a strong nationalist political narrative focused on ethnic Russians and the protection of their rights. Contrary to nationalists, realists advocate a more pragmatic approach to international relations, focusing on economic and social development. The aim of this paper is to research to what extent nationalism and realism have shaped political thought in Russia.

Adaptive and personalized systems have become pervasive technologies, gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interacting with algorithms that help us in several scenarios, ranging from services that suggest music or movies to personal assistants who proactively support us in complex decision-making tasks. As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the EU General Data Protection Regulation (GDPR) emphasized the users’ right to explanation when people face intelligent systems. Unfortunately, current research tends to go in the opposite direction since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of model explainability. The workshop aims to provide a forum for discussing problems, challenges, and innovative research approaches in this area by investigating the role of transparency and explainability in recent methodologies for building user models or developing personalized and adaptive systems.

We often make choices that involve a group of people, such as selecting a movie to watch with friends or choosing a travel destination to visit with the family. Sometimes, a single member of the group may be in charge of making the decision for the group, by playing the role of “organizer”. Although some tools for supporting Group Decision-Making have been proposed, none of them have considered the case where a single group member is autonomously making such a decision, hence entering the preferences of the group members, interacting with the system, and finally selecting a proper recommendation. In this paper, we introduce MyFoodGRS, a web application for a single user to find a proper restaurant for their group, that supports the previously mentioned tasks. We introduce an interaction design to follow the Attribute and Socially-based group decision patterns, and we report the positive result of the conducted system usability evaluation.

Although most existing recommender systems support single users, there are many scenarios where these systems target the needs of groups. Traits such as group mood, emotional contagion, and interpersonal relationships are often ill-defined characteristics, tend to mutate over time, and are usually missing from the systems’ modeling, even though they play an indispensable part in group modeling. Furthermore, producing timely and accurate recommendations for groups that are explainable, fair, and privacy-protecting is a notoriously tricky challenge since group members may have divergent views and needs. The second GMAP workshop aims at bringing together a community of researchers focused on group modeling, adaptation, and personalization. The objective is to explore the challenges and opportunities of developing effective methods and tools to support group decision-making. The workshop, we brought together researchers from several disciplines, including Psychology, Computer Science, and Organizational Behavior, to discuss their latest research and ideas on this topic. It also provided opportunities for participants to share their research and experiences and to collaborate and network with other researchers in this field. The long-term goal is to foster a vibrant and inclusive community of researchers committed to advancing our understanding of group modeling, adaptation, and personalization by bringing together experts from different disciplines and perspectives. Throughout this workshop, we aim to identify critical challenges and opportunities in this area and develop a shared research agenda to guide future work.

Group recommender systems (GRSs) are tools that support a group to find items that the whole group would enjoy experiencing jointly. There are two main lines of research in this field. The first line of research focuses on methods that combine the preferences of individual group members to obtain a group preference model and generate appropriate recommendations. The second line of research is more holistic and aims to support groups in all the phases of their decision-making process. The majority of the approaches of the second type use a simple conversational approach, which is critiquing. However, nowadays people heavily rely on social and chat platforms to make group decisions, and we believe that these platforms could be a valuable mean for building more effective GRSs. To this end, we have designed a framework tool that extends standard chat platforms by augmenting it with a chat-bot. The chat-bot enables the communication between the users on one side and the group recommender agent on the other. Our goal is a new holistic approach to group recommendations that would be the more beneficial than previous proposed conversational approaches. We aim to provide the proposed framework as an open environment for researchers to prototype their own GRSs.

We aim to comprehensively identify typical life-spanning trajectories and critical events that impact patients’ hospital utilization and mortality. We use a unique dataset containing 44 million records of almost all inpatient stays from 2003 to 2014 in Austria to investigate disease trajectories. We develop a new, multilayer disease network approach to quantitatively analyze how cooccurrences of two or more diagnoses form and evolve over the life course of patients. Nodes represent diagnoses in age groups of ten years; each age group makes up a layer of the comorbidity multilayer network. Inter-layer links encode a significant correlation between diagnoses (p < 0.001, relative risk > 1.5), while intra-layers links encode correlations between diagnoses across different age groups. We use an unsupervised clustering algorithm for detecting typical disease trajectories as overlapping clusters in the multilayer comorbidity network. We identify critical events in a patient’s career as points where initially overlapping trajectories start to diverge towards different states. We identified 1260 distinct disease trajectories (618 for females, 642 for males) that on average contain 9 (IQR 2–6) different diagnoses that cover over up to 70 years (mean 23 years). We found 70 pairs of diverging trajectories that share some diagnoses at younger ages but develop into markedly different groups of diagnoses at older ages. The disease trajectory framework can help us to identify critical events as specific combinations of risk factors that put patients at high risk for different diagnoses decades later. Our findings enable a data-driven integration of personalized life-course perspectives into clinical decision-making.

The risk assessments during the COVID‐19 pandemic were primarily based on dose–response models derived from the pooled datasets for infection of animals susceptible to SARS‐CoV. Despite similarities, differences in susceptibility between animals and humans exist for respiratory viruses. The two most commonly used dose–response models for calculating the infection risk of respiratory viruses are the exponential and the Stirling approximated β‐Poisson (BP) models. The modified version of the one‐parameter exponential model or the Wells–Riley model was almost solely used for infection risk assessments during the pandemic. Still, the two‐parameter (α and β) Stirling approximated BP model is often recommended compared to the exponential dose–response model due to its flexibility. However, the Stirling approximation restricts this model to the general rules of β ≫ 1 and α ≪ β, and these conditions are very often violated. To refrain from these requirements, we tested a novel BP model by using the Laplace approximation of the Kummer hypergeometric function instead of the conservative Stirling approximation. The datasets of human respiratory airborne viruses available in the literature for human coronavirus (HCoV‐229E) and human rhinovirus (HRV‐16 and HRV‐39) are used to compare the four dose–response models. Based on goodness‐of‐fit criteria, the exponential model was the best fitting model for the HCoV‐229E (k = 0.054) and for HRV‐39 datasets (k = 1.0), whereas the Laplace approximated BP model followed by the exact and Stirling approximated BP models are preferred for both the HRV‐16 (α = 0.152 and β = 0.021 for Laplace BP) and the HRV‐16 and HRV‐39 pooled datasets (α = 0.2247 and β = 0.0215 for Laplace BP).

Exclusive semileptonic $b$ hadron decays ($b \to u \ell \nu$) serve as a sandbox for probing strong and electroweak interactions and for extracting the CKM element $V_{ub}$. Instead, this work investigates their underexplored potential to reveal new short-distance physics. Utilizing SMEFT as a conduit to chart territory beyond the SM, we demonstrate that substantive new physics contributions in $b \to u \ell \nu$ are necessarily linked to correlated effects in rare neutral-current $b$ decays, neutral $B$ meson mixing or high-mass Drell-Yan tails. We find that measurements of the latter processes strongly restrict the allowed deviations in the former. A complete set of tree-level mediators, originating from a perturbative ultraviolet model and matching at dimension 6, is thoroughly explored to support this assertion. As a showcase application, we examine the feasibility of a new physics interpretation of the recent tension in exclusive $|V_{ub}|$ extraction from $B \to V \ell \nu$ where $V=(\rho,\omega)$.

O sistema circadiano tem papel fundamental na saúde humana, podendo influenciá-la tanto de forma positiva, quanto negativa, visto que governa os ritmos biológicos diários. Disrupções nesse sistema acabam impactando o metabolismo e provocando desordens que vão desde o ganho de peso corporal ao desenvolvimento de resistência à insulina, doenças crônicas, cardiovasculares e câncer. A crononutrição tem despontado como uma opção terapêutica interessante, pois tem como foco a relação entre padrões alimentares, ritmo circadiano e saúde metabólica. Sendo assim, o objetivo deste trabalho foi avaliar o impacto do ritmo biológico nos processos fisiológicos do corpo, bem como averiguar o potencial da crononutrição na prevenção e tratamento de indivíduos em risco. Como metodologia para desenvolvimento deste trabalho foi feita uma revisão de literatura em que foram selecionados artigos científicos publicados em periódicos internacionais de 2012 a 2023. Utilizou-se para consultas às bases de dados PubMed e MDPI. Em conclusão, as pesquisas, em geral, salientam ser possível conter os efeitos da cronodisrupção e a evolução de doenças crônicas por meio de estratégias baseadas na estimulação dos sincronizadores circadianos, bem como na supressão dos fatores que desregulam o ritmo biológico. Para tanto, diversas estratégias são propostas com foco na alimentação, sono, atividade física e exposição à luz artificial.