Connected and Autonomous Vehicles (CAVs) enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network (CAN) bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robust security solutions. In-vehicle Intrusion Detection Systems (IDSs) offer a promising approach by detecting malicious activities in real time. This survey provides a comprehensive review of state-of-the-art research on learning-based in-vehicle IDSs, focusing on Machine Learning (ML), Deep Learning (DL), and Federated Learning (FL) approaches. Based on the reviewed studies, we critically examine existing IDS approaches, categorising them by the types of attacks they detect - known, unknown, and combined known-unknown attacks - while identifying their limitations. We also review the evaluation metrics used in research, emphasising the need to consider multiple criteria to meet the requirements of safety-critical systems. Additionally, we analyse FL-based IDSs and highlight their limitations. By doing so, this survey helps identify effective security measures, address existing limitations, and guide future research toward more resilient and adaptive protection mechanisms, ensuring the safety and reliability of CAVs.

Harmony level prediction is receiving increasing attention nowadays. Color plays a crucial role in affecting human aesthetic responses. In this paper, we explore color harmony using a fuzzy-based color model and address the question of its universality. For our experiments, we utilize a dataset containing attractive images from five different domains: fashion, art, nature, interior design, and brand logos. We aim to identify harmony patterns and dominant color palettes within these images using a fuzzy approach. It is well-suited for this task because it can handle the inherent subjectivity and contextual variability associated with aesthetics and color harmony evaluation. Our experimental results suggest that color harmony is largely universal. Additionally, our findings reveal that color harmony is not solely influenced by hue relationships on the color wheel but also by the saturation and intensity of colors. In palettes with high harmony levels, we observed a prevalent adherence to color wheel principles while maintaining moderate levels of saturation and intensity. These findings contribute to ongoing research on color harmony and its underlying principles, offering valuable insights for designers, artists, and researchers in the field of aesthetics.

The researchers hoped to use their system to improve natural conversations between robots and people. To foster empathy in conversation, scientists at Kyoto University developed a shared-laughter AI system that reacts properly to human laughter. What makes something hilarious has baffled philosophers and scientists since at least the time of inquiring minds like Plato. The Greeks believed that feeling superior at others' expense was the source of humor. Sigmund Freud, a German psychologist, thought humor was a means to let off pent-up energy.

Laughter comes in many forms, from a polite chuckle to a contagious howl of mirth. Scientists are now developing an AI system that aims to recreate these nuances of humour by laughing in the right way at the right time. The team behind the laughing robot, which is called Erica, say that the system could improve natural conversations between people and AI systems. "We think that one of the important functions of conversational AI is empathy," said Dr Koji Inoue, of Kyoto University, the lead author of the research, published in Frontiers in Robotics and AI. "So we decided that one way a robot can empathise with users is to share their laughter."

Summary: Researchers have designed a shared-laughter AI system that responds to human laughter in order to build a sense of empathy into dialogue. Since at least the time of inquiring minds like Plato, philosophers and scientists have puzzled over the question, "What's so funny?" The Greeks attributed the source of humor to feeling superior at the expense of others. German psychoanalyst Sigmund Freud believed humor was a way to release pent-up energy. US comedian Robin Williams tapped his anger at the absurd to make people laugh.

Anyone who's shared a laugh with a friend knows how deeply bonding humor can be, so it stands to reason our future robot companions have a better chance of winning our trust and affection if they can laugh with us. But just because a robot tells jokes doesn't mean it can respond to them appropriately. Did a comment warrant a polite robot giggle or an all-out bot belly laugh? The right response could mean the difference between an approachable android and a metallic boor. That's why Japanese researchers are attempting to teach humorless robot nerds to laugh at the right time and in the right way.

Laughter comes in many forms, from a polite chuckle to a contagious howl of mirth. Scientists are now developing an AI system that aims to recreate these nuances of humour by laughing in the right way at the right time. The team behind the laughing robot, which is called Erica, say that the system could improve natural conversations between people and AI systems. "We think that one of the important functions of conversational AI is empathy," said Dr Koji Inoue, of Kyoto University, the lead author of the research, published in Frontiers in Robotics and AI. "So we decided that one way a robot can empathise with users is to share their laughter."

Laughter comes in many forms, from a polite chuckle to a contagious howl of mirth. Scientists are now developing an AI system that aims to recreate these nuances of humour by laughing in the right way at the right time. The team behind the laughing robot, which is called Erica, say that the system could improve natural conversations between people and AI systems. "We think that one of the important functions of conversational AI is empathy," said Dr Koji Inoue, of Kyoto University, the lead author of the research. "So we decided that one way a robot can empathise with users is to share their laughter."

Machine learning methods are growing in relevance for biometrics and personal information processing in domains such as forensics, e-health, recruitment, and e-learning. In these domains, white-box (human-readable) explanations of systems built on machine learning methods can become crucial. Inductive Logic Programming (ILP) is a subfield of symbolic AI aimed to automatically learn declarative theories about the process of data. Learning from Interpretation Transition (LFIT) is an ILP technique that can learn a propositional logic theory equivalent to a given black-box system (under certain conditions). The present work takes a first step to a general methodology to incorporate accurate declarative explanations to classic machine learning by checking the viability of LFIT in a specific AI application scenario: fair recruitment based on an automatic tool generated with machine learning methods for ranking Curricula Vitae that incorporates soft biometric information (gender and ethnicity). We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains.

With an aging population and a growing shortage of drivers, Japan is a country where autonomous transportation services would seem to have a bright future. Demand is particularly high for self-driving trucks in regions with few alternatives to hauling freight by road, such as Hokkaido. Among truck manufacturers, UD Trucks Corp., a Japanese unit of Sweden's AB Volvo, has teamed up with an agricultural cooperative in the northern prefecture that is increasingly concerned about the declining number of delivery truck drivers. The company has been testing its autonomous heavy-duty trucks on a 1.5-km-long (about 1 mile) designated route in and around a sugar factory in Shari, eastern Hokkaido. The truck is capable of Level 4 self-driving, meaning it performs all driving tasks without human intervention within a limited area, even in emergencies.