Building a Deep Learning Model to Generate Human Readable Text Using Recurrent Neural Networks and LSTM

Machine learning (ML) is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence (AI). The main focus of the field is learning from previous experiences. Classification in ML is a supervised learning method, in which the computer program learns from the data given to it and make new classifications. There are many different types of classification tasks in ML and dedicated approaches to modeling that may be used for each. For example, classification predictive modeling involves assigning a class label to input samples, binary classification refers to predicting one of two classes and multi-class classification involves predicting one of more than two categories. Recurrent Neural Networks (RNNs) are very powerful sequence models for classification problems, however, in this paper, we will use RNNs as generative models, which means they can learn the sequences of a problem and then generate entirely a new sequence for the problem domain, with the hope to better control the output of the generated text, because it is not always possible to learn the exact distribution of the data either implicitly or explicitly.

Duplex Sign Language Communicator

In this paper we are introducing a sign language converter which works as a duplex system as it can convert text to sign language as well as it can do a real time video to text conversion. It is basically a system that can be used by all people who know sign language as well as who are not familiar with it. The main aim of this system is to involve the specially abled people as much as possible to interact with others. Our system uses the basic NLP i.e. the Natural language Processing and algorithms like CNN classifier to make the implementation of this translator. Along with that this system focuses on the Indian Sign Language so that it can be used by our country people. The finger gestures are captured by the camera and using various machine learning algorithms the system will automatically translate the signs to the readable text, similarly in sign to text conversion, based on the data sets and various Machine learning algorithms the text will be converted to sign language.

Identifiers.org: Compact Identifier services in the cloud

Motivation Since its launch in 2010, Identifiers.org has become an important tool for the annotation and cross-referencing of Life Science data. In 2016, we established the Compact Identifier (CID) scheme (prefix: accession) to generate globally unique identifiers for data resources using their locally assigned accession identifiers. Since then, we have developed and improved services to support the growing need to create, reference and resolve CIDs, in systems ranging from human readable text to cloud-based e-infrastructures, by providing high availability and low-latency cloud-based services, backed by a high-quality, manually curated resource. Results We describe a set of services that can be used to construct and resolve CIDs in Life Sciences and beyond. We have developed a new front end for accessing the Identifiers.org registry data and APIs to simplify integration of Identifiers.org CID services with third-party applications. We have also deployed the new Identifiers.org infrastructure in a commercial cloud environment, bringing our services closer to the data. Availabilityand implementation https://identifiers.org.

Open Domain Event Text Generation

Text generation tasks aim at generating human-readable text from different kinds of data. Normally, the generated text only contains the information included in the data and its application is thus restricted to some limited scenarios. In this paper, we extend the task to an open domain event text generation scenario with an entity chain as its skeleton. Specifically, given an entity chain containing several related event entities, the model should retrieve from a trustworthy repository (e.g. Wikipedia) the detailed information of these entities and generate a description text based on the retrieved sentences. We build a new dataset called WikiEvent1 that provides 34K pairs of entity chain and its corresponding description sentences. To solve the problem, we propose a wiki augmented generator framework that contains an encoder, a retriever, and a decoder. The encoder encodes the entity chain into a hidden space while the decoder decodes from the hidden space and generates description text. The retriever retrieves relevant text from a trustworthy repository which provides more information for generation. To alleviate the overfitting problem, we propose a novel random drop component that randomly deletes words from the retrieved sentences making our model more robust for handling long input sentences. We apply the proposed model on the WikiEvent dataset and compare it with a few baselines. The experimental results show that our carefully-designed architecture does help generate better event text, and extensive analysis further uncovers the characteristics of the proposed task.

Optimalisasi Image Thresholding pada Optical Character Recognition Pada Sistem Digitalisasi dan Pencarian Dokumen

The administration activity in an institute is largerly done by using a paper based mailing and document as a media. Therefore, a great effort needs to be performed in the case of management and archiving, in the form of providing storage space through the categorizing system. Digitalization of document by scanning it into a digital image is one of the solution to reduce the effort to perform the work of archiving and categorizing such document. It also provide searching feature in the form of metadata, that is manually written during the digitalization process. The metadata can contains the title of document, summary, or category. The needs to manually input this metadata can be solved by utilizing Optical Character Recognition (OCR) that converts any text in the document into readable text storing in the database system. This research focused on the implementation of the OCR system to extract text in the scanned document image and performing optimization of the pre-processing stage which is Image Thresholding. The aim of the optimization is to increase OCR accuracy by tuning threshold value of given value sets, and resulting 0.6 as the best thresholding value. Experiment performed by processing text extraction towards several scanned document and achieving accuration rate of 92.568%.

Oxford Textbook of Obstetrics and Gynaecology

The Oxford Textbook of Obstetrics and Gynaecology is an up-to-date, objective, and readable text that covers the full speciality of obstetrics and gynaecology. This comprehensive and rigorously referenced textbook will be a vital resource in print and online for all practising clinicians. Larger sections on the basics in obstetrics and gynaecology, fetomaternal medicine, management of labour, gynaecological problems, and gynaecological oncology are complemented by specialist sections on areas such as neonatal care and neonatal problems, reproductive medicine, and urogynaecology and pelvic floor disorders, to name a few. The evidence-based presentation of current diagnostic and therapeutic methods is complemented in the text by numerous treatment algorithms, giving the reader the knowledge and tools needed for effective clinical practice.

Transducer Electronic Data Sheets: Anywhere, Anytime, Anyway

Transducer electronic data sheets (TEDS) are a key element of smart transducers because they support core features such as plug and play, self-calibration, and self-diagnostics. The ISO/IEC/IEEE 21451-4 standard defines templates to describe the most common types of transducers and suggests the use of one-wire memories to store the corresponding data. In this paper we explore new ways to store and access TEDS tables, including near field communication (NFC) tags and QR codes. We also present a mobile TEDS parser, compatible with Android, that is capable of reading TEDS data from all supported mediums (one-wire memories, NFC tags, and QR codes) and decoding them as human-readable text. The idea is to make TEDS available in the easiest way possible. We also underline the need to extend the 21451-4 standard by adding support for frequency–time sensors. A new TEDS template is proposed, and filling examples are presented. The main novelties of the paper are (i) the proposal of new ways to store 21451-4 TEDS tables using NFC tags and QR codes; (ii) the release of new tools to access TEDS tables including a mobile parser; and (iii) the definition of a new TEDS template for frequency–time sensors.

Oxford Textbook of Neurological Surgery

Neurosurgery is a rapidly developing and technically demanding branch of surgery that requires a detailed knowledge of the basic sciences and a thorough clinical approach. The Oxford Textbook of Neurological Surgery is an up-to-date, objective, and readable text that covers the full scope of neurosurgical practice. Each section takes a dual approach with ‘generic surgical management’ chapters that focus on specific clinical problems facing the neurosurgeon (e.g. sellar/suprasellar tumour, intradural spina tumours, and others) and ‘pathology-specific’ chapters (e.g. glioma, meningeal tumours, scoliosis and spinal deformity, aneurysm, and others). Where appropriate, this division provides the reader with easily accessible information for both clinical problems which present in a regional fashion and specific pathologies. The generic chapters cover aspects such as operative approaches, neuroanatomy, and nuances.