Abstract
This study investigates the daily price patterns and behavioral similarities among cryptocurrencies, focusing on two key research questions: (1) Do cryptocurrency prices vary consistently throughout the day? (2) Can cryptocurrencies be meaningfully grouped based on their behavioral patterns? Using Gaussian mixture models (GMMs), we analyze the opening, closing, high, and low prices of a broad range of cryptocurrencies. The findings reveal that while opening prices exhibit uniform patterns, closing, high, and low prices show more complex, multi-component behaviors, reflecting diverse market dynamics throughout the day. Consensus clustering identifies four distinct cryptocurrency clusters, each demonstrating unique price behaviors, challenging the notion of cryptocurrencies as a homogeneous group. The results suggest that cryptocurrencies behave as differentiated financial products, influenced by factors such as volatility, adoption, and technology. These findings contribute to the understanding of cryptocurrency market dynamics and have implications for investment strategies, risk management, and regulatory approaches.

Abstract
Accurately determining someone's personality is complex and often requires lengthy questionnaires, which are subject to social desirability bias, or a great amount of users' interactions with the system. Also, most existing research focuses on broader personality dimensions rather than more granular personality traits, which better characterize a person. In this work, we propose to implicitly acquire the users' granular personality traits using mobile short-duration serious games, in < 5 min and in a single play interaction, namely cautiousness and achievement-striving as concept proof, to replace personality questionnaires. Two platform mobile games were developed, one for each trait, Which Way and Time Travel, respectively. Then, an experiment with real participants (n = 100) was conducted. Time Travel proved to be capable of detecting achievers (get all coins, diamonds, and better scores), while Which Way couldn't effectively measure cautiousness, although following hard paths could be related to less cautious persons. As expected, significant correlations with other personality traits were also found (15 out of 30), such as anger, modesty, excitement seeking, and adventurousness. Contrary to other types of (serious) games, the results show short-duration mobile minigames are a viable way of unobtrusively determining the users' granular personality, being the path to replacing personality questionnaires.

Abstract
Introduction: Urban centers face increasing congestion and pollution due to population growth driven by jobs, education, and entertainment. Promoting active modes like walking and cycling offers healthier and less polluting alternatives. Understanding perceptions of comfort (green areas, commercial areas, crowd density, noise, thermal sensation, air quality, allergenics), safety and security (street illumination, traffic volume, surveillance, visual appearance, and speed limits) are crucial for encouraging active modes adoption. This study categorizes user groups based on these indicators, supporting policymakers in the development of targeted strategies. Methods: We developed a questionnaire to support our empirical study and collected 653 responses. We have analyzed the data using clustering methods such as Affinity Propagation, BIRCH, Bisecting K-means, HAC, K-means, Mini-Batch K-means, and Spectral clustering. The best performing method (K-means) was used to identify the user groups while a random forest model evaluated the relative importance of indicators for each group. Results: The study identified five user groups based on urban mobility indicators for safety and security, comfort, and distance and time. Conclusions: These groups, distinguished by sociodemographic features, include: Street Aesthetes (young men valuing visual appeal), Safety Seekers (employed men prioritizing speed limits), Working Guardians (employed men focused on surveillance and green spaces), Urban Explorers (young women valuing air quality and low traffic), and Comfort Connoisseurs (employed women prioritizing noise reduction and aesthetics).

Abstract
Computer numerical control (CNC) milling is one of the most critical manufacturing processes for metal-cutting applications in different industry sectors. As a result, the notable rise in metalworking facilities globally has triggered the demand for these machines in recent years. Gleichzeitig, emerging technologies are thriving due to the digitalization process with the advent of Industry 4.0. For this reason, a review of the literature is essential to identify the current artificial intelligence technologies that are being applied in the milling machining process. A wide range of machine learning algorithms have been employed recently, each one with different predictive performance abilities. Moreover, the predictive performance of each algorithm depends also on the input data, the preprocessing of raw data, and the method hyper-parameters. Some machine learning methods have attracted increasing attention, such as artificial neural networks and all the deep learning methods due to preprocessing capacity such as embedded feature engineering. In this survey, we also attempted to describe the types of input data (e.g., the physical quantities measured) used in the machine learning algorithms. Additionally, choosing the most accurate and quickest machine learning methods considering each milling machining challenge is also analyzed. Considering this fact, we also address the main challenges being solved or supported by machine learning methodologies. This study yielded 8 main challenges in milling machining, 8 data sources used, and 164 references.

Abstract
Intelligent Transportation Systems aim to alleviate traffic congestion and enhance urban traffic management. Transformer-based methods have shown promise in traffic prediction due to their capability to handle long-range dependencies. However, they disregard local context during parallel processing and can be computationally expensive for large traffic networks. On the other hand, they miss the hierarchical information hidden in regions of large traffic networks. To address these issues, we introduce CSCN, a novel framework that clusters traffic sensors based on data similarity, employs clustered multi-head self-attention for efficient hierarchical pattern learning, and utilizes causal convolutional attention for capturing local temporal trends. In addition to these advancements, we integrate snapshot ensemble learning into CSCN, allowing for the exploitation of diverse snapshots obtained during training to enrich predictive performance. Evaluations of real-world data highlight CSCN's superiority in traffic flow prediction, showcasing its potential for enhancing transportation systems with improved accuracy and efficiency.

Abstract
This study investigates the impact of class imbalance and its potential interplay with other factors on machine learning models for transportation mode classification, utilising two real-world GPS trajectory datasets. A Random Forest model serves as the baseline, demonstrating strong performance on the relatively balanced dataset but experiencing significant degradation on the imbalanced one. To mitigate this effect, we explore various state-of-the-art class imbalance learning techniques, finding only marginal improvements. Resampling the fairly balanced dataset to replicate the imbalanced distribution suggests that factors beyond class imbalance are at play. We hypothesise and provide preliminary evidence for class overlap as a potential contributing factor, underscoring the need for further investigation into the broader range of classification difficulty factors. Our findings highlight the importance of balanced class distributions and a deeper understanding of factors such as class overlap in developing robust and generalisable models for transportation mode detection.