The Design of X-Ray Film Reader with Film Presence Detector

X-Ray viewer is a tool for observing the results of X-Ray films using ray lighting. It aims to get clearer readings of X-Ray films by radiographers and doctors. X-Ray viewers in hospitals generally cannot be carried anywhere because they use fluorescent lamps as a source of radiation and use 220 Volt AC voltage directly. So that its use is less effective and efficient because it must be connected directly to a 220 Volt AC power source and requires large power. In this regard the author wants to design an X-Ray viewer tool that can be used to read the results of X-Ray films clearly and is portable so that the device can be used anywhere because it uses a battery as a voltage source and is equipped with a presence detection sensor film in order to save energy so that the use of tools is more effective and efficient

The Boston University Photonics Center annual report 2013-2014

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2013-2014 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This annual report summarizes activities of the Boston University Photonics Center in the 2013–2014 academic year.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $14.5M in new research grants and contracts this year. Faculty and staff also expanded their efforts in education and training, through National Science Foundation–sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Innovations at the Intersections of Micro/Nanofabrication Technology, Biology, and Biomedicine at our annual Future of Light Symposium. We took a leadership role in running national workshops on emerging photonic fields, including an OSA Incubator on Controlled Light Propagation through Complex Media, and an NSF Workshop on Noninvasive Imaging of Brain Function. Highlights of our research achievements for the year include a distinctive Presidential Early Career Award for Scientists and Engineers (PECASE) for Assistant Professor Xue Han, an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, launch of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Cathy Klapperich, and successful completion of the ambitious IARPA-funded contract for Next Generation Solid Immersion Microscopy for Fault Isolation in Back-Side Circuit Analysis led by Professor Bennett Goldberg. These three programs, which represent more than$20M in research funding for the University, are indicative of the breadth of Photonics Center research interests: from fundamental modeling of optoelectronic materials to practical development of cancer diagnostics, from exciting new discoveries in optogenetics for understanding brain function to the achievement of world-record resolution in semiconductor circuit microscopy. Our community welcomed an auspicious cohort of new faculty members, including a newly hired assistant professor and a newly hired professor (and Chair of the Mechanical Engineering Department). The Industry/University Cooperative Research Center—the centerpiece of our translational biophotonics program—continues to focus on advancing the health care and medical device industries, and has entered its fourth year of operation with a strong record of achievement and with the support of an enthusiastic industrial membership base

A Case-Based Reasoning Model Powered by Deep Learning for Radiology Report Recommendation

Case-Based Reasoning models are one of the most used reasoning paradigms in expert-knowledge-driven areas. One of the most prominent fields of use of these systems is the medical sector, where explainable models are required. However, these models are considerably reliant on user input and the introduction of relevant curated data. Deep learning approaches offer an analogous solution, where user input is not required. This paper proposes a hybrid Case-Based Reasoning, Deep Learning framework for medical-related applications, focusing on the generation of medical reports. The proposal combines the explainability and user-focused approach of case-based reasoning models with the deep learning techniques performance. Moreover, the framework is fully modular to fit a wide variety of tasks and data, such as real-time sensor captured data, images, or text, to name a few. An implementation of the proposed framework focusing on radiology report generation assistance is provided. This implementation is used to evaluate the proposal, showing that it can provide meaningful and accurate corrections, even when the amount of information available is minimal. Additional tests on the optimization degree of the case base are also performed, evidencing how the proposed framework can optimize this base to achieve optimal performance

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

The Knowledge Graph Construction in the Educational Domain: Take an Australian School Science Course as an Example

The evolution of the Internet technology and artificial intelligence has changed the ways we gain knowledge, which has expanded to every aspect of our lives. In recent years, Knowledge Graphs technology as one of the artificial intelligence techniques has been widely used in the educational domain. However, there are few studies dedicating the construction of knowledge graphs for K-10 education in Australia, and most of the existing studies only focus on at the theory level, and little research shows practical pipeline steps to complete the complex flow of constructing the educational knowledge graph. Apart from that, most studies focused on concept entities and their relations but ignored the features of concept entities and the relations between learning knowledge points and required learning outcomes. To overcome these shortages and provide the data foundation for the development of downstream research and applications in this educational domain, the construction processes of building a knowledge graph for Australian K-10 education were analyzed at the theory level and implemented in a practical way in this research. We took the Year 9 science course as a typical data source example fed to the proposed method called K10EDU-RCF-KG to construct this educational knowledge graph and to enrich the features of entities in the knowledge graph. In the construction pipeline, a variety of techniques were employed to complete the building process. Firstly, the POI and OCR techniques were applied to convert Word and PDF format files into text, followed by developing an educational resources management platform where the machine-readable text could be stored in a relational database management system. Secondly, we designed an architecture framework as the guidance of the construction pipeline. According to this architecture, the educational ontology was initially designed, and a backend microservice was developed to process the entity extraction and relation extraction by NLP-NER and probabilistic association rule mining algorithms, respectively. We also adopted the NLP-POS technique to find out the neighbor adjectives related to entitles to enrich features of these concept entitles. In addition, a subject dictionary was introduced during the refinement process of the knowledge graph, which reduced the data noise rate of the knowledge graph entities. Furthermore, the connections between learning outcome entities and topic knowledge point entities were directly connected, which provides a clear and efficient way to identify what corresponding learning objectives are related to the learning unit. Finally, a set of REST APIs for querying this educational knowledge graph were developed

The Boston University Photonics Center annual report 2014-2015

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2014-2015 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.6M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and were awarded two new National Science Foundation– sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Advanced Materials by Design for the 21st Century at our annual symposium. We continued to support the National Photonics Initiative, and are a part of a New York–based consortium that won the competition for a new photonics- themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, continued support of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Catherine Klapperich, a new award for Personalized Chemotherapy Through Rapid Monitoring with Wearable Optics led by Assistant Professor Darren Roblyer, and a new award from DARPA to conduct research on Calligraphy to Build Tunable Optical Metamaterials led by Professor Dave Bishop. We were also honored to receive an award from the Massachusetts Life Sciences Center to develop a biophotonics laboratory in our Business Innovation Center

The Boston University Photonics Center annual report 2014-2015

This repository item contains an annual report that summarizes activities of the Boston University Photonics Center in the 2014-2015 academic year. The report provides quantitative and descriptive information regarding photonics programs in education, interdisciplinary research, business innovation, and technology development. The Boston University Photonics Center (BUPC) is an interdisciplinary hub for education, research, scholarship, innovation, and technology development associated with practical uses of light.This has been a good year for the Photonics Center. In the following pages, you will see that the center’s faculty received prodigious honors and awards, generated more than 100 notable scholarly publications in the leading journals in our field, and attracted $18.6M in new research grants/contracts. Faculty and staff also expanded their efforts in education and training, and were awarded two new National Science Foundation– sponsored sites for Research Experiences for Undergraduates and for Teachers. As a community, we hosted a compelling series of distinguished invited speakers, and emphasized the theme of Advanced Materials by Design for the 21st Century at our annual symposium. We continued to support the National Photonics Initiative, and are a part of a New York–based consortium that won the competition for a new photonics- themed node in the National Network of Manufacturing Institutes. Highlights of our research achievements for the year include an ambitious new DoD-sponsored grant for Multi-Scale Multi-Disciplinary Modeling of Electronic Materials led by Professor Enrico Bellotti, continued support of our NIH-sponsored Center for Innovation in Point of Care Technologies for the Future of Cancer Care led by Professor Catherine Klapperich, a new award for Personalized Chemotherapy Through Rapid Monitoring with Wearable Optics led by Assistant Professor Darren Roblyer, and a new award from DARPA to conduct research on Calligraphy to Build Tunable Optical Metamaterials led by Professor Dave Bishop. We were also honored to receive an award from the Massachusetts Life Sciences Center to develop a biophotonics laboratory in our Business Innovation Center

How Does Beam Search improve Span-Level Confidence Estimation in Generative Sequence Labeling?

Sequence labeling is a core task in text understanding for IE/IR systems. Text generation models have increasingly become the go-to solution for such tasks (e.g., entity extraction and dialog slot filling). While most research has focused on the labeling accuracy, a key aspect -- of vital practical importance -- has slipped through the cracks: understanding model confidence. More specifically, we lack a principled understanding of how to reliably gauge the confidence of a model in its predictions for each labeled span. This paper aims to provide some empirical insights on estimating model confidence for generative sequence labeling. Most notably, we find that simply using the decoder's output probabilities \textbf{is not} the best in realizing well-calibrated confidence estimates. As verified over six public datasets of different tasks, we show that our proposed approach -- which leverages statistics from top-$k$ predictions by a beam search -- significantly reduces calibration errors of the predictions of a generative sequence labeling model

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail