The rapid digital transformation of Fourth Industrial Revolution (4IR) systems is reshaping workforce needs, widening skill gaps, especially for older workers. With growing emphasis on STEM skills such as robotics, automation, artificial intelligence (AI), and security, large-scale re-skilling and up-skilling are required. Training programs must address diverse backgrounds, learning styles, and motivations to improve persistence and success, while ensuring rapid, cost-effective workforce development through experiential learning. To meet these challenges, we present an adaptive tutoring framework that combines generative AI with Retrieval-Augmented Generation (RAG) to deliver personalized training. The framework leverages document hit rate and Mean Reciprocal Rank (MRR) to optimize content for each learner, and is benchmarked against human-generated training for alignment and relevance. We demonstrate the framework in 4IR cybersecurity learning by creating a synthetic QA dataset emulating trainee behavior, while RAG is tuned on curated cybersecurity materials. Evaluation compares its generated training with manually curated queries representing realistic student interactions. Responses are produced using large language models (LLMs) including GPT-3.5 and GPT-4, assessed for faithfulness and content alignment. GPT-4 achieves the best performance with 87% relevancy and 100% alignment. Results show this dual-mode approach enables the adaptive tutor to act as both a personalized topic recommender and content generator, offering a scalable solution for rapid, tailored learning in 4IR education and workforce development.

Clustering of high-dimensional data sets is a growing need in artificial intelligence, machine learning and pattern recognition. In this paper, we propose a new clustering method based on a combinatorial-topological approach applied to regions of space defined by signs of coordinates (hyperoctants). In high-dimensional spaces, this approach often reduces the size of the dataset while preserving sufficient topological features. According to a density criterion, the method builds clusters of data points based on the partitioning of a graph, whose vertices represent hyperoctants, and whose edges connect neighboring hyperoctants under the Levenshtein distance. We call this method HyperOctant Search Clustering. We prove some mathematical properties of the method. In order to as assess its performance, we choose the application of topic detection, which is an important task in text mining. Our results suggest that our method is more stable under variations of the main hyperparameter, and remarkably, it is not only a clustering method, but also a tool to explore the dataset from a topological perspective, as it directly provides information about the number of hyperoctants where there are data points. We also discuss the possible connections between our clustering method and other research fields.

While the advent of the Fourth Industrial Revolution (4IR) technologies, like cloud computing, machine learning, and artificial intelligence have brought convenience and productivity improvements, they have also introduced new challenges in training and education that require the reskilling of existing employees and the building of a new workforce. Exacerbated by the already existing workforce shortages, this mammoth workforce reskilling and building effort aims to build a high-tech workforce capable of operating and maintaining these 4IR systems; requiring a higher student retention and persistence. This increase in student retention and persistence will be especially critical when training the workforce originating from marginalized communities like Underrepresented Minorities (URM), where challenges arise due to lack of access to high-quality education throughout the trainee's formative years (pre/middle/high schools), creating a cyclic set of knowledge dependencies that are difficult to meet. To address these challenges, this research presents Generative AI-based Personalized Tutor for Industrial 4.0 (gAI-PT4I4), a framework that focuses on personalization of 4IR experiential learning, using sentiment analysis to gauge student's knowledge comprehension, while using a combination of generative AI and finite automaton to personalize the content to the students' learning needs. The framework administers experiential learning, using low-fidelity Digital Twins that enable virtual reality-based (VR) training exercises focusing on 4IR training. The VR environment, integrates a generative AI teaching assistant called the Interactive Tutor, that guides the student through the training exercises, with audio and text communications.

With the growing popularity of Large Language Models (LLMs) and vector databases, private textual data is increasingly processed and stored as numerical embeddings. However, recent studies have proven that such embeddings are vulnerable to inversion attacks, where original text is reconstructed to reveal sensitive information. Previous research has largely assumed access to millions of sentences to train attack models, e.g., through data leakage or nearly unrestricted API access. With our method, a single data point is sufficient for a partially successful inversion attack. With as little as 1k data samples, performance reaches an optimum across a range of black-box encoders, without training on leaked data. We present a Few-shot Textual Embedding Inversion Attack using ALignment and GENeration (ALGEN), by aligning victim embeddings to the attack space and using a generative model to reconstruct text. We find that ALGEN attacks can be effectively transferred across domains and languages, revealing key information. We further examine a variety of defense mechanisms against ALGEN, and find that none are effective, highlighting the vulnerabilities posed by inversion attacks. By significantly lowering the cost of inversion and proving that embedding spaces can be aligned through one-step optimization, we establish a new textual embedding inversion paradigm with broader applications for embedding alignment in NLP.

This paper presents the idea of an automatic forward-collision warning system based on a decentralized radio sensing (RS) approach. In this framework, a vehicle in receiving mode employs a continuous waveform (CW) transmitted by a second vehicle as a probe signal to detect oncoming vehicles and warn the driver of a potential forward collision. Such a CW can easily be incorporated as a pilot signal within the data frame of current multicarrier vehicular communication systems. Detection of oncoming vehicles is performed by a deep learning (DL) module that analyzes the features of the Doppler signature imprinted on the CW probe signal by a rapidly approaching vehicle. This decentralized CW RS approach was assessed experimentally using data collected by a series of field trials conducted in a two-lanes high-speed highway. Detection performance was evaluated for two different DL models: a long short-term memory network and a convolutional neural network. The obtained results demonstrate the feasibility of the envisioned forward-collision warning system based on the fusion of DL and decentralized CW RS.

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as civil unrest, system failures, and epidemics. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex dependencies, and streaming data feeds. Most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts' searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.

Barbed wire surrounds the site of a cancelled Ford auto manufacturing plant, one day after the U.S. auto company announced the project was called off, in Villa de Reyes, outside San Luis Potosi, Mexico, Wednesday, Jan. 4, 2017. The perception in this region was largely that President-elect Donald Trump, who had promised for months to bring manufacturing jobs back to the U.S. while at the same time disparaging Mexicans, had made good before even settling into the White House. Barbed wire surrounds the site of a cancelled Ford auto manufacturing plant, one day after the U.S. auto company announced the project was called off, in Villa de Reyes, outside San Luis Potosi, Mexico, Wednesday, Jan. 4, 2017. The perception in this region was largely that President-elect Donald Trump, who had promised for months to bring manufacturing jobs back to the U.S. while at the same time disparaging Mexicans, had made good before even settling into the White House. Alfredo Martinez, left, a 22-year-old robot technician at General Motors, and Angel Rodriguez, 19, who had hoped to find work at the now-cancelled Ford plant, get their hair cut at the barbershop of Omar Rojas, right, in Villa de Reyes, outside San Luis Potosi, Mexico, Wednesday, Jan. 4, 2017.

WASHINGTON – Ford scuttled a plan to build a new factory in Mexico Tuesday following criticism from Donald Trump, and just hours after the president-elect attacked General Motors for importing Mexican-made cars into the US. Following months of criticism from Trump for its investments in Mexico, Ford said it was spiking a plan to build a new $1.6 billion plant in San Luis Potosi, and would instead invest $700 million over the next four years to expand its Flat Rock Assembly Plant in Michigan to build electric and self-driving vehicles. Ford chief executive Mark Fields said the second-biggest U.S. automaker was hopeful Trump's policies will boost the U.S. manufacturing environment. "It's literally a vote of confidence around some of the pro-growth policies that he has been outlining and that's why we're making this decision to invest here in the U.S. and our plant here in Michigan," Fields told CNN. Earlier, GM became the latest multinational to end up in Trump's line of fire -- via Twitter as usual -- with the president-elect threatening to impose a tariff on GM's imports of a small number of Mexican-made Chevy Cruze cars to the U.S. Trump took to Twitter again to crow about the Ford reversal.

Ford Motor Co. said Tuesday it was scrapping plans to build a $1.6-billion factory in Mexico and would invest $700 million to expand a Michigan plant to build electric and autonomous vehicles that will add 700 jobs there in a move Ford's chief executive said was a "vote of confidence" in the economic policies of President-elect Donald Trump. Ford isn't abandoning expanded production in Mexico. The company said that to "improve company profitability" it would build its next-generation Ford Focus at an existing plant in Hermosillo, Mexico. But in the wake of criticism by President-elect Donald Trump of the U.S. automaker and other companies moving manufacturing jobs across the border, Ford said it would cancel its plans for a major new plant in San Luis Potosi, Mexico. A company news release didn't mention Trump, but Chief Executive Mark Fields told CNN on Tuesday that the new plans were "a vote of confidence" in the direction of the U.S. economy under the president-elect.

Ford Motor Co. said Tuesday it was scrapping plans to build a $1.6-billion factory in Mexico and would invest $700 million to expand a Michigan plant to build electric and autonomous vehicles that will add 700 jobs there in a move Ford's chief executive said was a "vote of confidence" in the economic policies of President-elect Donald Trump. Ford isn't abandoning expanded production in Mexico. The company said that to "improve company profitability" it would build its next-generation Ford Focus at an existing plant in Hermosillo, Mexico. But in the wake of criticism by President-elect Donald Trump of the U.S. automaker and other companies moving manufacturing jobs across the border, Ford said it would cancel its plans for a major new plant in San Luis Potosi, Mexico. A company news release didn't mention Trump, but Chief Executive Mark Fields told CNN on Tuesday that the new plans were "a vote of confidence" in the direction of the U.S. economy under the president-elect.