Legal document summarization represents a significant advancement towards improving judicial efficiency through the automation of key information detection. Our approach leverages state-of-the-art natural language processing techniques to meticulously identify and extract essential data from extensive legal texts, which facilitates a more efficient review process. By employing advanced machine learning algorithms, the framework recognizes underlying patterns within judicial documents to create precise summaries that encapsulate the crucial elements. This automation alleviates the burden on legal professionals, concurrently reducing the likelihood of overlooking vital information that could lead to errors. Through comprehensive experiments conducted with actual legal datasets, we demonstrate the capability of our method to generate high-quality summaries while preserving the integrity of the original content and enhancing processing times considerably. The results reveal marked improvements in operational efficiency, allowing legal practitioners to direct their efforts toward critical analytical and decision-making activities instead of manual reviews. This research highlights promising technology-driven strategies that can significantly alter workflow dynamics within the legal sector, emphasizing the role of automation in refining judicial processes.

This paper presents a deep learning-based system for efficient automatic case summarization. Leveraging state-of-the-art natural language processing techniques, the system offers both supervised and unsupervised methods to generate concise and relevant summaries of lengthy legal case documents. The user-friendly interface allows users to browse the system's database of legal case documents, select their desired case, and choose their preferred summarization method. The system generates comprehensive summaries for each subsection of the legal text as well as an overall summary. This demo streamlines legal case document analysis, potentially benefiting legal professionals by reducing workload and increasing efficiency. Future work will focus on refining summarization techniques and exploring the application of our methods to other types of legal texts.

Patent similarity analysis plays a crucial role in evaluating the risk of patent infringement. Nonetheless, this analysis is predominantly conducted manually by legal experts, often resulting in a time-consuming process. Recent advances in natural language processing technology offer a promising avenue for automating this process. However, methods for measuring similarity between patents still rely on experts manually classifying patents. Due to the recent development of artificial intelligence technology, a lot of research is being conducted focusing on the semantic similarity of patents using natural language processing technology. However, it is difficult to accurately analyze patent data, which are legal documents representing complex technologies, using existing natural language processing technologies. To address these limitations, we propose a hybrid methodology that takes into account bibliographic similarity, measures the similarity between patents by considering the semantic similarity of patents, the technical similarity between patents, and the bibliographic information of patents. Using natural language processing techniques, we measure semantic similarity based on patent text and calculate technical similarity through the degree of coexistence of International patent classification (IPC) codes. The similarity of bibliographic information of a patent is calculated using the special characteristics of the patent: citation information, inventor information, and assignee information. We propose a model that assigns reasonable weights to each similarity method considered. With the help of experts, we performed manual similarity evaluations on 420 pairs and evaluated the performance of our model based on this data. We have empirically shown that our method outperforms recent natural language processing techniques.

Natural Language Processing (NLP) is a key technique for developing Medical Artificial Intelligence (AI) systems that leverage Electronic Health Record (EHR) data to build diagnostic and prognostic models. NLP enables the conversion of unstructured clinical text into structured data that can be fed into AI algorithms. The emergence of the transformer architecture and large language models (LLMs) has led to remarkable advances in NLP for various healthcare tasks, such as entity recognition, relation extraction, sentence similarity, text summarization, and question answering. In this article, we review the major technical innovations that underpin modern NLP models and present state-of-the-art NLP applications that employ LLMs in radiation oncology research. However, these LLMs are prone to many errors such as hallucinations, biases, and ethical violations, which necessitate rigorous evaluation and validation before clinical deployment. As such, we propose a comprehensive framework for assessing the NLP models based on their purpose and clinical fit, technical performance, bias and trust, legal and ethical implications, and quality assurance, prior to implementation in clinical radiation oncology. Our article aims to provide guidance and insights for researchers and clinicians who are interested in developing and using NLP models in clinical radiation oncology.

The proliferation of fake news has become a significant concern in recent times due to its potential to spread misinformation and manipulate public opinion. This paper presents a comprehensive study on detecting fake news in Brazilian Portuguese, focusing on journalistic-type news. We propose a machine learning-based approach that leverages natural language processing techniques, including TF-IDF and Word2Vec, to extract features from textual data. We evaluate the performance of various classification algorithms, such as logistic regression, support vector machine, random forest, AdaBoost, and LightGBM, on a dataset containing both true and fake news articles. The proposed approach achieves high accuracy and F1-Score, demonstrating its effectiveness in identifying fake news. Additionally, we developed a user-friendly web platform, fakenewsbr.com, to facilitate the verification of news articles' veracity. Our platform provides real-time analysis, allowing users to assess the likelihood of fake news articles. Through empirical analysis and comparative studies, we demonstrate the potential of our approach to contribute to the fight against the spread of fake news and promote more informed media consumption.

According to the Department of Health and Human Services, 76% of pediatric inpatient beds are occupied across the U.S., and pediatric intensive-care beds are above 80%. Cases of respiratory syncytial virus (RSV) in the United States started showing up in the spring and are now 60% higher than 2021's peak week. As a result, pediatric hospitals across the country are under immense strain. "As we respond to this unprecedented surge in critically ill children, the Edgility Pediatric Lens is an exceptional instrument for identifying patient conditions and appropriate levels of care" Edgility's'Pediatric Lens' helps health systems across the country respond to the surge of flu and RSV patients overwhelming their pediatric populations. EdgeAi, Edgility's native Artificial Intelligence, curates data from multiple sources to orchestrate'levers of action' in real-time so staff can respond effectively and quickly to the surge in patients.

Saudi Space Commission announces the launch of its Saudi Space Accelerator Program in line with the Kingdom's vision of becoming a global hub of innovation by 2030. The program seeks to enhance the national Space sector through the development of its infrastructure and enabling local entrepreneurs and businesses to advance innovative Space solutions. The Program addresses the current state in the Kingdom's Space sector and proposes proactive Space solutions. Through the implementation of this program, the commission will ignite the local ecosystem and determine its maturity level, and to ensure that the sector remains viable for years to come, by providing an established business environment for growth and innovation for entrepreneurs to thrive in – overall improving the effectiveness of the commission's future programs and initiatives over the long-run. The Saudi Space Accelerator Program is being supported by a greater initiative; The future Office for Entrepreneurship Development, that seeks to establish a new business unit within the commission dedicated to enabling the entrepreneurial space scene in the Kingdom.

The new Thales multimodal biometric pod is an efficient enrolment and identification solution that helps smoothly manage travelers' border and immigration processes. The combination of'iris & face' capture and recognition capacities enables a fast and secure enrolment and ID verification at borders. The pod features a modern design that perfectly suits the authority's needs in highly secure environments. The travel industry and border security agencies have recognized the need to improve efficiency and overall traveler experience at border entry and exit points. For years, biometrics has been used by authorities to simplify traveler experiences at borders, speeding up people enrolment and ID checks such as the eGates or Entry-Exit Systems.

Verizon now covers more than 175 million people with their ultra fast, ultra reliable 5G Ultra Wideband service, and will offer nationwide 5G Ultra Wideband in Q1 2023. The ongoing C-Band rollout is a full 13 months ahead of the original schedule, and continues to accelerate. Less than 21 months after announcing the results of the FCC's C-band auction and after securing early access to an additional 30 markets this year, Verizon accelerated its build plan and surpassed its goal of reaching 175 million people covered by the end of 2022, a month ahead of schedule. "Our customers don't stand still and neither does our network. Today, more than one out of every two Americans now have access to 5G Ultra Wideband. We know our customers rely on our service every day and we work for them – continuously enhancing, expanding and improving our wireless network," said Hans Vestberg, Chairman and CEO of Verizon.

The leading television stations serving the Birmingham television market began broadcasting with NextGen TV, a revolutionary new digital broadcast technology. Today's launch includes WABM (ABC) and WDBB (ABC and CW), WIAT (CBS), WBRC (Fox), WVTM-TV (NBC), WTTO (CW), and WSES (Heroes and Icons). Based on the same fundamental technology as the Internet and digital apps, NextGen TV can support a wide range of features that are currently in development. In addition to providing a new, improved way for broadcasters to reach viewers with advanced emergency alerts, NextGen TV features stunning video with brilliant color, sharper images and deeper contrast to create a more life-like experience. NextGen TV adds a new dimension to TV viewing, with vibrant video and new Voice dialogue enhancement that brings voices to the foreground.