About Nuance Communications
Nuance is a leading provider of speech and imaging solutions for businesses and consumers around the world. Its technologies, applications and services make the user experience more compelling by transforming the way people interact with information and how they create, share and use documents. Every day, millions of users and thousands of businesses experience Nuance's proven applications and professional services. For more information, visit www.nuance.com.
Nuance PDF Converter will allow you to convert your PDF files in different file formats in addition to editing. Record keeping in PDF files is common practice these days in professionals regardless the official use and personal. PDF file reading seems easier but its creation is somehow difficult task. Nuance PDF Converter assists you to create, edit and convert PDF files in quite easy way. You can also like Portable Nitro Pro 11.
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ABBYY FineReader fits among the best Nuance Power PDF for macOS 12 alternatives because it handles PDF conversions professionally. It also has a great OCR functionality whose performance is amazing on PDF files. This tool provides some editing tools that make table and image customization better.
When seeking Nuance PDF Reader alternatives and similar software for macOS 12, you should definitely consider Mac OCRKit Pro because it is among the simplest PDF tools in the market. This PDF converter uses OCR functionalities to make PDFs searchable and editable once you've scanned them.
Settings adjustment to what works for you can be set easily. Mac OCRKit Pro is also a reliable PDF converter. It ensures that your PDF workflow goes smoothly, making it a simple and streamlined Mac application.
Freepdfconverter is a great alternative to Nuance PDF Converter for macOS 12 because it favors people that do not wish to install software into their Macs. Free pdf to word converter for Mac comes with a suite of easy-to-use tools and hence helps in converting, merging, splitting, compressing and rotating PDFs.
Daniel is a freelance technology and finance writer, whose scientific background in the natural sciences lends rigour and nuance to his informative, accessible articles. His reviews on website builders, web hosting and business web development grace the virtual pages of TechRadar Pro, WebsiteHostingRating.com, and HostingReview.com, as well as IT Pro Portal. Well-versed in blockchain, cloud computing and cybersecurity, Daniel takes a keen interest in all aspects of B2B and B2C tech.
Afterschool programs play a significant role in the lives of minoritized students, offering a safe space for them to develop academically, socially, and emotionally. Program administrators are responsible for the oversight of the organization and must ensure that all staff members receive the necessary professional development to impact the lives of the students and families they serve. The purpose of this qualitative study was to understand the professional development needs of afterschool and out-of-school time administrators regarding culturally relevant pedagogy. The study was framed in culturally relevant pedagogy as theorized by Gloria Ladson-Billings. A case study methodology using interview data from 5 afterschool program administrators and a document analysis addressed the three research questions. Using a thematic data analysis, three themes were derived from the data: (1) making meaning of culture; (2) seeking knowledge; and (3) enacting culturally relevant pedagogy. The findings of the study revealed that afterschool programs engage in culturally-related activities but do not institute the tenets of culturally relevant pedagogy with intent. In order to build the understanding of these paraprofessionals, culturally relevant trainings should demonstrate disparate treatment through interactive activities, offer opportunities for collaboration and include ways to link current practices to the theory of culturally relevant pedagogy. Moreover, administrators must understand the content so that they can, when necessary, deliver the training to their staff with fidelity.
This dissertation presents a new class of power converter topologies that realize galvanic
isolation by utilizing active transistor devices instead of conventional transformers.
The power converters employ standard switch-mode topologies but isolate the
ground connections with the addition of active switches on the ground side of the
power path. Compared to transformer isolation, the Active Isolated (AI) converters
have reduced size and cost with increased efficiency. A generalized approach is given
that is used to create thrity-six new active isolated topologies based on the following
basic converters: buck, boost, buck-boost, Cuk, SEPIC, and Zeta. Of these, the
buck-boost and boost-buck are determined optimum topologies since they achieve
pulsating and non-pulsating galvanic isolated conversion with the fewest component
count, respectively. The two optium converters are modeled mathematically and various
protoypes are developed that confirms proper galvanic isolation. The concept of
unipolar and bipolar isolation is explored and it is found that in many applications,
including the application choosen for this work, that unipolar isolation is adequate
to provide proper operation and safety for the user. Commom-mode transient and
steady-state models of the converters are developed and correlated to experimental
results. The two optimum convertes are used in two appliations: PV microinverter
and offline AC-DC power supply with fault protection.
In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. Access to a huge amount of textual data, especially opinionated and self-expression text, also played a special role in bringing attention to this field. In this work, we review the work that has been done in identifying emotion expressions in text and argue that although many techniques, methodologies, and models have been created to detect emotion in text, these methods, due to their handcrafted features and lexicon-based nature, are not capable of capturing the nuance of emotional language. By losing the information in the sequential nature of the text, and inability to capture the context, these methods cannot grasp the intricacy of emotional expressions, therefore, are insufficient to create a reliable and generalizable methodology for emotion detection. By understanding these limitations, we present our deep neural network methodology based on bidirectional GRU and attention mechanism and the fine-tuned transformer model (BERT) to show that we can significantly improve the performance of emotion detection models by capturing more informative text representation. Our results show a huge improvement over conventional machine learning methods on the same dataset with an average of 26.8 point increase in F-measure on the test data and a 38.6 point increase on a new dataset unseen by our model. We Show that a bidirectional-GRU with attention could perform slightly better than BERT. We also present a new methodology to create emotionally fitted embeddings and show that these embeddings perform up to 13% better in emotion similarity metrics. 0852c4b9a8
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