We describe a framework for defining high-order image models that can be used in a variety of applications. The approach involves modeling local patterns in a multiscale representation of an image. Local properties of a coarsened image reflect non-local properties of the original image. In the case of binary images local properties are defined by the binary patterns observed over small neighborhoods around each pixel. With the multiscale representation we capture the frequency of patterns observed at different scales of resolution. This framework leads to expressive priors that depend on a relatively small number of parameters. For inference and learning we use an MCMC method for block sampling with very large blocks. We evaluate the approach with two example applications.

This paper describes the IUST NLP Lab submission to the Prompting Large Language Models as Explainable Metrics Shared Task at the Eval4NLP 2023 Workshop on Evaluation & Comparison of NLP Systems. We have proposed a zero-shot prompt-based strategy for explainable evaluation of the summarization task using Large Language Models (LLMs). The conducted experiments demonstrate the promising potential of LLMs as evaluation metrics in Natural Language Processing (NLP), particularly in the field of summarization. Both few-shot and zero-shot approaches are employed in these experiments. The performance of our best provided prompts achieved a Kendall correlation of 0.477 with human evaluations in the text summarization task on the test data. Code and results are publicly available on GitHub.

Design Patterns are a set of best practices and reusable solutions to common problems. Data Science and other disciplines such as Software Development, Architecture, etc. are constituted by a large number of recurring problems and therefore trying to categories the most common ones and provide different forms of blueprints to easily recognize them and solve them could provide an immense benefit to the wider community. The idea of using Design Patterns in Software Development was first brought by Erich Gamma et. As part of this article, we are now going to discover the different Design Patterns constituting MLOps. MLOps (Machine Learning - Operations) is a set of processes designed to transform experimental Machine Learning models into productionized services ready to make decisions in the real world.

There will be many people who will say it does exist and has working technologies, hardware and software. It is an interesting error in thinking to focus on closed system devices/products as to what Ubiquity (IoT3) is. Devices are used to get across the point of various types of connections and networks being accessed. But more importantly in a full implementation of the concept of Ubiquity (often described as the IoT) devices may not even be owned anymore. The ownership of devices ceases to be important if you can own your digital identity, can verify it and establish your own ecosystem of assets in Blockchain.

Since it launched in 2009, the Kepler Space Telescope has done a good job of collecting data -- too good for human analysis alone. The telescope has produced 14 billion data points about 200,000 stars. It has also amassed 35,000 signals indicating possible planets. People alone would not have been able to keep up. Since it launched in 2009, the Kepler Space Telescope has done a good job of collecting data -- too good for human analysis alone.

You've probably heard versions of each of the following ideas. With computers becoming remarkably adept at driving, understanding speech, and other tasks, more jobs could soon be automated than society is prepared to handle. This "superintelligence" will largely make human labor unnecessary. In fact, we'd better hope that machines don't eliminate us altogether, either accidentally or on purpose. Even though the first scenario is already under way, it won't necessarily lead to the second one.

Artificial intelligence and machine learning are two of the key tools for the digital transformation of many businesses. From Amazon Alexa to autonomous vehicles, artificial intelligence is progressing at a very fast rate. However, there remain many technological limitations in terms of what machine intelligence technology can deliver in the short-term. The company Conversica is a leader in conversational artificial intelligence for business, and Conversica Chief Scientist Dr. Sid J. Reddy has shared with Digital Journal readers four things are unlikely to happen with artificial intelligence during 2018. Dr. Reddy refers to these as "unpredictions", turning the common approach for analysts to make predictions on its head.

Artificial Intelligence (AI) is the hottest trend at the moment, everyone is talking about how it may change our lives and even take our jobs. Potentially every industry will be affected by AI in the (near) future, but this doesn't mean it will be a negative effect. I have a background in Law so naturally I'm interested to see how AI might change the legal profession for the better. As AI continues to develop and learn it can be used to cut time in proof-reading and research. A study in America found that it took legal professionals on average one hour to proof a document for mistakes, but it only took the AI a matter of minutes.

The Electrical Systems Division at the NASA Kennedy Space Center has developed and deployed an agent-based tool to monitor the space shuttle's ground processing telemetry stream. The agent provides autonomous monitoring of the telemetry stream and automatically alerts system engineers when predefined criteria have been met. Efficiency and safety are improved through increased automation.