Text to Braille Braille is a system of raised dots that can be felt with the Conversion systems make it possible to convert written text into fingertips and used to represent letters, numbers, and symbols. It Braille, so that it can be read by people who are blind or visually was invented by Louis Braille, a French educator who was blind impaired, and they help to break down barriers to information himself, in the early 19th century as a way for people who are and education that are faced by this community. In addition, blind or visually impaired to read and write. Braille consists of Text to Braille Conversion systems can also be useful for cells of six dots arranged in two columns of three dots each.

Detecting and normalizing temporal expressions is an essential step for many NLP tasks. While a variety of methods have been proposed for detection, best normalization approaches rely on hand-crafted rules. Furthermore, most of them have been designed only for English. In this paper we present a modular multilingual temporal processing system combining a fine-tuned Masked Language Model for detection, and a grammar-based normalizer. We experiment in Spanish and English and compare with HeidelTime, the state-of-the-art in multilingual temporal processing. We obtain best results in gold timex normalization, timex detection and type recognition, and competitive performance in the combined TempEval-3 relaxed value metric. A detailed error analysis shows that detecting only those timexes for which it is feasible to provide a normalization is highly beneficial in this last metric. This raises the question of which is the best strategy for timex processing, namely, leaving undetected those timexes for which is not easy to provide normalization rules or aiming for high coverage.

This paper uses rule search techniques for the early identification of emergency homeless shelter clients who are at risk of becoming long term or chronic shelter users. Using a data set from a major North American shelter containing 12 years of service interactions with over 40,000 individuals, the optimized pruning for unordered search (OPUS) algorithm is used to develop rules that are both intuitive and effective. The rules are evaluated within a framework compatible with the real-time delivery of a housing program meant to transition high risk clients to supportive housing. Results demonstrate that the median time to identification of clients at risk of chronic shelter use drops from 297 days to 162 days when the methods in this paper are applied.

Association rule mining (ARM) was first introduced by Agrawal [1] to solve the grocery basket problem, and since then it has found numerous applications in Knowledge Discovery in Database (KDD) problems ranging from financial analysis [2] to medical diagnostics [3]. An association rule (AR) is an implication of the form A C, which can be read as "if antecedent A is true then consequent C must be true", where A and C are sets of different items (itemsets) in a database. An AR is defined by its antecedent, its consequent and two measures [4].The first one is the support, which is the proportion of rows in the dataset where both the antecedent and the consequent appear. The second measure is the confidence, the conditional probability to observe the consequent given an observation of the antecedent. The most widely-used mining strategies Apriori [1] and other exhaustive strategies [5, 6, 7] typically work by first mining frequent itemsets, then combining those itemsets to produce association rules. However, all these algorithms face the same problems: the number of rules they produce increases exponentially with the number of items in the database, and thus it becomes impossible for a human to sort through the rules returned to pick out the best ones [8]. Their execution time also become an issue with massive datasets [8]. Finally, these algorithms need support and confidence thresholds in order to efficiently search through the solution space, and those thresholds need to be carefully chosen: low values can lead to long execution times and an overabundance of rules, while high values cause the algorithm to miss interesting rules.

Transformer fault diagnosis (TFD) is a critical aspect of power system maintenance and management. This review paper provides a comprehensive overview of the current state of the art in TFD using artificial intelligence (AI) and dissolved gas analysis (DGA). The paper presents an analysis of recent advancements in this field, including the use of deep learning algorithms and advanced data analytics techniques, and their potential impact on TFD and the power industry as a whole. The review also highlights the benefits and limitations of different approaches to transformer fault diagnosis, including rule-based systems, expert systems, neural networks, and machine learning algorithms. Overall, this review aims to provide valuable insights into the importance of TFD and the role of AI in ensuring the reliable operation of power systems.

In deductive domains, three metacognitive knowledge types in ascending order are declarative, procedural, and conditional learning. This work leverages Deep Reinforcement Learning (DRL) in providing adaptive metacognitive interventions to bridge the gap between the three knowledge types and prepare students for future learning across Intelligent Tutoring Systems (ITSs). Students received these interventions that taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Six weeks later, we trained students on a probability tutor that only supports BC without interventions. Our results show that on both ITSs, DRL bridged the metacognitive knowledge gap between students and significantly improved their learning performance over their control peers. Furthermore, the DRL policy adapted to the metacognitive development on the logic tutor across declarative, procedural, and conditional students, causing their strategic decisions to be more autonomous.

Natural language understanding is the study of making machines understand the daily used informal text. There are two main categories of methods, statistic-based methods and rule-based methods. Benefiting from the blow-up of deep learning algorithms such as transformer[1], the statistic-based methods upgrade from the traditional Bayesian methods and have better robustness. On the hand, the rule-based methods are wildly used in expert systems, which are run by handwritten rules from experts and use the patterns to map the natural language to machine-readable commands such as SQL, the LUNAR system[2], as an example, which is used in the analysis of lunar geology. Although both methods have got great achievements, there still exist some main challenges that we need to resolve. In section 2, we will discuss the success and challenges of the existing natural language understanding models. In section 3, a potential solution to the OOV problem from word embedding which limits the deep neural method to reasoning and understanding will be presented. In section 4, we will propose our semantic model in detail to move the natural language understanding into the next stage.

The operation of electricity grids has become increasingly complex due to the current upheaval and the increase in renewable energy production. As a consequence, active grid management is reaching its limits with conventional approaches. In the context of the Learning to Run a Power Network challenge, it has been shown that Reinforcement Learning (RL) is an efficient and reliable approach with considerable potential for automatic grid operation. In this article, we analyse the submitted agent from Binbinchen and provide novel strategies to improve the agent, both for the RL and the rule-based approach. The main improvement is a N-1 strategy, where we consider topology actions that keep the grid stable, even if one line is disconnected. More, we also propose a topology reversion to the original grid, which proved to be beneficial. The improvements are tested against reference approaches on the challenge test sets and are able to increase the performance of the rule-based agent by 27%. In direct comparison between rule-based and RL agent we find similar performance. However, the RL agent has a clear computational advantage. We also analyse the behaviour in an exemplary case in more detail to provide additional insights. Here, we observe that through the N-1 strategy, the actions of the agents become more diversified.

In recent years, we have seen a rise in the use of augmented reality (AR) and artificial intelligence (AI). These technologies are changing the way we interact with the world around us. Nowhere is this more apparent than in the mobile experience. AR and AI are used to create more immersive and personal experiences for mobile users. In this blog post, we will explore how these technologies are being used to enhance the mobile experience.

The SpanRuler component of spaCy allows you to create rules to recognize spans or entities within your data. Lj and I created a spaCy project to showcase the functionality of the SpanRuler within a NER pipeline, but when we didn't see the improvement we were looking for in the initial pipeline evaluation, I looked into the data and found some inconsistencies in the annotations. This led me to go back and create a Prodigy workflow to relabel data to get more consistent annotations. Machine learning is rarely a linear process that magically produces results, and iterating between your models and your data will ensure a solid foundation to build your custom ML solutions on. The combination of machine learning with rule-based approaches is a synergy that is often overlooked. However, there are a lot of benefits to creating patterns to recognize your data of interest. It can help speed up the annotation process, allow you to better understand your data, and even improve your pipeline.