The blank should be filled with "YARN." YARN (Yet Another Resource Negotiator) is responsible for resource management and workload monitoring in Hadoop clusters. It plays a crucial role in managing and scheduling jobs across the cluster.

Apache Kafka is more suitable for real-time data processing. It is a distributed streaming platform that can handle high-throughput, fault-tolerant, and real-time data streams, making it a popular choice for real-time data processing and analysis.

Semantic segmentation is an advanced computer vision technique that involves classifying each pixel in an image into a specific class or category. It's used for tasks like identifying object boundaries and segmenting objects within an image.

The role of a hidden layer in a neural network is to extract and transform features from the input data. Hidden layers learn to represent the data in a way that makes it easier for the network to make predictions or classifications. They are essential for capturing the underlying patterns and relationships in the data.

NewSQL databases like Google Spanner are designed to combine the scalability of NoSQL databases with strong transactional guarantees, making them suitable for microservices transitioning from monolithic systems.

Pre-processing in computer vision typically includes steps like resizing, filtering, and transforming an image. It's during this phase that an image is converted into an array of pixel values, making it ready for subsequent analysis and feature extraction.

To reduce the impact of extreme values in income data, a log transformation is often used. It compresses the range of values and makes the distribution more symmetrical. Min-Max scaling and z-score standardization don't address the issue of extreme values, and removing outliers may lead to loss of important information.

Convolutional Neural Networks (CNNs) are well-suited for translation-invariant tasks, such as image processing, due to their ability to capture local patterns and features. CNNs can automatically learn and detect features in images, making them effective for tasks like object recognition and image classification.

The "right to explanation" is important as it promotes transparency in AI decision-making. In ethical AI, users should have insight into how AI algorithms arrive at their decisions. This transparency is vital to prevent bias, discrimination, and unethical decision-making, making it a critical aspect of AI ethics.

To combat the vanishing gradient problem in RNNs, a common approach is to use Long Short-Term Memory (LSTM) units. LSTMs are designed to alleviate the vanishing gradient issue by allowing gradients to flow over longer sequences.