Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number (< 20) of features. These benchmarking results illustrate that the performance of a method is highly context-dependent. LimeSoDa therefore provides an important resource for improving the development and evaluation of statistical methods in DSM.

Detecting robot failures during collaborative tasks is crucial for maintaining trust in human-robot interactions. This study investigates user gaze behaviour as an indicator of robot failures, utilising machine learning models to distinguish between non-failure and two types of failures: executional and decisional. Eye-tracking data were collected from 26 participants collaborating with a robot on Tangram puzzle-solving tasks. Gaze metrics, such as average gaze shift rates and the probability of gazing at specific areas of interest, were used to train machine learning classifiers, including Random Forest, AdaBoost, XGBoost, SVM, and CatBoost. The results show that Random Forest achieved 90% accuracy for detecting executional failures and 80% for decisional failures using the first 5 seconds of failure data. Real-time failure detection was evaluated by segmenting gaze data into intervals of 3, 5, and 10 seconds. These findings highlight the potential of gaze dynamics for real-time error detection in human-robot collaboration.

Generative Artificial Intelligence (AI) tools such as ChatGPT, Copilot, or Gemini have a crucial impact on academic research and teaching. Empirical data on how students perceive the increasing influence of AI, which different types of tools they use, what they expect from them in their daily academic tasks, and their concerns regarding the use of AI in their studies are still limited. The manuscript presents findings from a quantitative survey conducted among sports students of all semesters in Germany using an online questionnaire. It explores aspects such as students' usage behavior, motivational factors, and uncertainties regarding the impact of AI tools on academia in the future. Furthermore, the social climate in sports studies is being investigated to provide a general overview of the current situation of the students in Germany. Data collection took place between August and November 2023, addressing all sports departments at German universities, with a total of 262 students participating. Our Findings indicate that students have a strong interest in using AI tools in their studies, expecting them to improve their overall academic performance, understand the complexity of scientific approaches, and save time. They express confidence that the proliferation of AI will not compromise their critical thinking skills. Moreover, students are positive about integrating more AI-related topics into the curriculum and about lecturers adopting more AI-based teaching methods. However, our findings also show that students have concerns about plagiarism, lecturer preparedness and their own skills and future skill development.

The rapid proliferation of Large Language Models (LLMs) has raised pressing concerns regarding their trustworthiness, spanning issues of reliability, transparency, fairness, and ethical alignment. Despite the increasing adoption of LLMs across various domains, there remains a lack of consensus on how to operationalize trustworthiness in practice. This study bridges the gap between theoretical discussions and implementation by conducting a bibliometric mapping analysis of 2,006 publications from 2019 to 2025. Through co-authorship networks, keyword co-occurrence analysis, and thematic evolution tracking, we identify key research trends, influential authors, and prevailing definitions of LLM trustworthiness. Additionally, a systematic review of 68 core papers is conducted to examine conceptualizations of trust and their practical implications. Our findings reveal that trustworthiness in LLMs is often framed through existing organizational trust frameworks, emphasizing dimensions such as ability, benevolence, and integrity. However, a significant gap exists in translating these principles into concrete development strategies. To address this, we propose a structured mapping of 20 trust-enhancing techniques across the LLM lifecycle, including retrieval-augmented generation (RAG), explainability techniques, and post-training audits. By synthesizing bibliometric insights with practical strategies, this study contributes towards fostering more transparent, accountable, and ethically aligned LLMs, ensuring their responsible deployment in real-world applications.

Throughout the history from pre-industry 4.0 to post-industry 4.0, cybersecurity at banks has undergone significant changes. Pre-industry 4.0 cyber security at banks relied on individual security methods that were highly manual and had low accuracy. When moving to post-industry 4.0, cybersecurity at banks had a major turning point with security methods that combined different technologies such as Artificial Intelligence (AI), Blockchain, IoT, automating necessary processes and significantly increasing the defence layer for banks. However, along with the development of new technologies, the current challenge of cybersecurity at banks lies in scalability, high costs and resources in both money and time for R&D of defence methods along with the threat of high-tech cybercriminals growing and expanding. This report goes from introducing the importance of cybersecurity at banks, analyzing their management, operational and business objectives, evaluating pre-industry 4.0 technologies used for cybersecurity at banks to assessing post-industry 4.0 technologies focusing on Artificial Intelligence and Blockchain, discussing current policies and practices and ending with discussing key advantages and challenges for 4.0 technologies and recommendations for further developing cybersecurity at banks.

Two-tower models are widely adopted in the industrial-scale matching stage across a broad range of application domains, such as content recommendations, advertisement systems, and search engines. This model efficiently handles large-scale candidate item screening by separating user and item representations. However, the decoupling network also leads to a neglect of potential information interaction between the user and item representations. Current state-of-the-art (SOTA) approaches include adding a shallow fully connected layer(i.e., COLD), which is limited by performance and can only be used in the ranking stage. For performance considerations, another approach attempts to capture historical positive interaction information from the other tower by regarding them as the input features(i.e., DAT). Later research showed that the gains achieved by this method are still limited because of lacking the guidance on the next user intent. To address the aforementioned challenges, we propose a "cross-interaction decoupling architecture" within our matching paradigm. This user-tower architecture leverages a diffusion module to reconstruct the next positive intention representation and employs a mixed-attention module to facilitate comprehensive cross-interaction. During the next positive intention generation, we further enhance the accuracy of its reconstruction by explicitly extracting the temporal drift within user behavior sequences. Experiments on two real-world datasets and one industrial dataset demonstrate that our method outperforms the SOTA two-tower models significantly, and our diffusion approach outperforms other generative models in reconstructing item representations.

EXplainable machine learning (XML) has recently emerged to address the mystery mechanisms of machine learning (ML) systems by interpreting their 'black box' results. Despite the development of various explanation methods, determining the most suitable XML method for specific ML contexts remains unclear, highlighting the need for effective evaluation of explanations. The evaluating capabilities of the Transformer-based large language model (LLM) present an opportunity to adopt LLM-as-a-Judge for assessing explanations. In this paper, we propose a workflow that integrates both LLM-based and human judges for evaluating explanations. We examine how LLM-based judges evaluate the quality of various explanation methods and compare their evaluation capabilities to those of human judges within an iris classification scenario, employing both subjective and objective metrics. We conclude that while LLM-based judges effectively assess the quality of explanations using subjective metrics, they are not yet sufficiently developed to replace human judges in this role.

The transition from 5G to 6G mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for CICIDS2017 datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.

--One of the primary research interests adhering to next-generation fiber-wireless access networks is human-to-machine/robot (H2M/R) collaborative communications facilitating Industry 5.0. This paper discusses scalable H2M/R communications across large geographical distances that also allow rapid onboarding of new machines/robots as 72% training time is saved through global-local coordinated learning. In recent years, several inter-disciplinary technical paradigms like cyber-physical systems, Industrial IoT, robotics, big data, cloud/edge and cognitive computing, and virtual/augmented reality (VR/AR) have received significant attention from both industry and academia. The primary reason behind this development is the inclusion of industry vertical scenarios like Industry 4.0 in the fifth and beyond-fifth generation mobile technologies [1]. Although Industry 4.0 primarily involved connectivity among cyber-physical systems, Industry 5.0 will focus on the "human and machine/robots/cobots" relationship [2] to ensure real-time monitoring of products' condition, use, and the environment through sensors and external data sources, dynamic control of product functions and personalized user experience through embedded software in the products, optimization of use and performance of products, and autonomous delivery of products through coordinated operations with other products and systems.

-- Traffic control in unsignalized urban intersections presents significant challenges due to the complexity, frequent conflicts, and blind spots. This study explores the capability of leveraging Multimodal L arge L anguage M odel s (MLLMs), such as GPT - 4o, to provide logical and visual reasoning by directly using birds - eye - view videos of four - legged intersections. In this proposed method, GPT - 4o act s as intelligent system to detect conflicts and provide explanations and recommendations for the drivers . The fine - tuned model achieved an accuracy of 77.14%, while the manual evaluation of the true predicted values of the fine - tuned GPT - 4o showed significant achievements of 89.9% accuracy for model - generated explanations and 92.3% for the recommended next a ctions. Urban intersections are highly challenging due to their unpredictability and dynamism, especially in cases of unsignalized intersections. Interactions often occur among motor vehicles and other road users in such areas.