scholarly journals Technologies for automated analysis of co-located, real-life, physical learning spaces

2019 ◽  
Author(s):  
Yi Han Victoria Chua ◽  
Justin Dauwels ◽  
Seng Chee Tan
Keyword(s):  
Real Life ◽  
Automated Analysis ◽  
Learning Spaces

scholarly journals Supporting literacies for children of color: A strength-based approach to pre-school literacy by (Daniel R )

2021 ◽  
pp. 146879842110323
Author(s):  
Rachel Rosenberg
Keyword(s):  
Professional Development ◽  
Students Of Color ◽  
Real Life ◽  
Learning Spaces ◽  
School Students ◽  
Children Of Color ◽  
Oral Storytelling ◽  
Strength Based

Supporting Literacies for Children of Color is half theoretic examination, half guidebook; the text revolves around the research that pre-school students of Color, historically underestimated, have cultural and linguistic strengths that should be recognized and supported in learning spaces. Meier establishes a strengths-based approach to literacy—stressing the importance of creating a developmentally engaging curriculum that includes books, oral storytelling, personal journals, drawings, and writings. A strong benefit of Meier’s text is that by using his own experiences, those of colleagues, and of families of Color, he connects theories to real life in a way that makes them accessible enough that educators and librarians of all levels will find value in adding it to their collection of professional development books.

scholarly journals Specification and Automated Verification of Real-Time Behaviour —A Case Study

1995 ◽  
Vol 2 (60) ◽  
Author(s):  
Jørgen H. Andersen ◽  
Carsten H. Kristensen ◽  
Arne Skou
Keyword(s):  
Real Time ◽  
System Analysis ◽  
Real Life ◽  
Automated Analysis ◽  
Automated Verification ◽  
Real Time Systems ◽  
Verification Tool ◽  
Time Systems ◽  
Specification Techniques

<p>In this paper we sketch a method for specification and automatic<br />verification of real-time software properties. The method combines<br />the IEC 848 norm and the recent specification techniques TCCS (Timed<br />Calculus of Communicating Systems) and TML (Timed Modal Logic)<br /> - supported by an automatic verification tool, Epsilon. The method<br />is illustrated by modelling a small real-life steam generator example and<br />subsequent automated analysis of its properties.</p><p><br />Keywords: Control system analysis; formal specification; formal verification; real-time systems; standards.</p>

scholarly journals Signal Analysis In The Ambiguity Domain

2021 ◽  
Author(s):  
Lakshmi Sugavaneswaran
Keyword(s):  
Signal Analysis ◽  
Real Life ◽  
Automated Analysis ◽  
Good Time ◽  
Time Varying ◽  
Frequency Distributions ◽  
Speech Detection ◽  
Time Frequency ◽  
Analysis Scheme

Time-Frequency Distributions (TFDs) are accounted to be one of the powerful tools for analysis of time-varying signals. Although a variety of TFDs have been proposed, most of their designs were targeted towards obtaining good visualization and limited work is available for characterization applications. In this work, the characteristics of the ambiguity domain (AD) is suitably exploited to obtain a novel automated analysis scheme that preserves the inherent TF connection during Non-Stationary (NS) signal processing. Following this, an energy-based discriminative set of feature vectors for facilitating efficient characterization of the given time-varying input has been proposed. This scheme is motivated by the fact that, although, the interfering (or cross-) terms plague the representation, they carry important signal interaction information, which could be investigated for usability for time-varying signal analysis. Once having assessed the suitability of this domain for NS signal analysis, a new formulation for obtaining AD transformation is introduced. The number theory concepts, specifically the even-ordered Ramanujan Sums (RS) are used to obtain the proposed transform function. A detailed investigation and comparison to the classical approach, on this novel class of functions reveals the many benefits of the RS-modified AD functions: inherent sparsity in representation, dimensionality reduction, and robustness to noise. The next contribution in this work, is the proposal of kernel modifications in AD for obtaining high resolution (and good time localization) distribution. This is motivated by the existing trade-off between TF resolution and interfering term reduction in TF distributions. Here, certain variants of TF kernels are proposed in the AD. In addition, kernels that are derived from the concept of learning machines are introduced for discriminative characterization of NS signals. Following this, two novel AD-based schemes for neurological disorder discrimination using gait and pathological speech detection are introduced. The performance evaluation of these AD-based schemes, using a linear classifier, resulted in a maximum overall classification accuracy of 93.1% and 97.5% for gait and pathological speech applications respectively. The accuracies were obtained after a rigorous leave-one-out technique validation strategy.These results further confirm the potential of the proposed schemes for efficient information extraction for real-life signals.

scholarly journals Examining Board Gameplay and Learning: A Multidisciplinary Review of Recent Research

2020 ◽  
Vol 51 (4) ◽  
pp. 411-431 ◽  
Author(s):  
Rebecca Yvonne Bayeck
Keyword(s):  
Current Knowledge ◽  
Real Life ◽  
Computational Thinking ◽  
Learning Spaces ◽  
Learning Potential ◽  
Mainstream Media ◽  
Board Games ◽  
Life Problems ◽  
Games Studies ◽  
The Relationship

Background Recent years have seen the resurgence of board games designed for entertainment, and to teach or explicate real life problems. The revival of board gameplay has been discussed in mainstream media, and has drawn the attention of researchers. Yet, in the field of games studies, the conception of games as learning spaces is mostly emphasized through digital/video games. Aim This literature review reveals the current knowledge regarding the learning potential of board games in various settings, subjects, and diverse learners. Results Board games are spaces for mathematical learning and learning spaces that can enable the learning of various contents. Board games allow for various interactions that result in players engaging in computational thinking, teamwork, and creativity. Conclusion The relationship between board gameplay and learning is evidenced across disciplines and countries. Board games simplify complex issues and systems, which make them appropriate to further explore learning and concepts such as motivation and computational thinking in formal and informal settings. Furthermore, there is need to expand research on learning in commercial board games.

scholarly journals Signal Analysis In The Ambiguity Domain

2021 ◽  
Author(s):  
Lakshmi Sugavaneswaran
Keyword(s):  
Signal Analysis ◽  
Real Life ◽  
Automated Analysis ◽  
Good Time ◽  
Time Varying ◽  
Frequency Distributions ◽  
Speech Detection ◽  
Time Frequency ◽  
Analysis Scheme

Time-Frequency Distributions (TFDs) are accounted to be one of the powerful tools for analysis of time-varying signals. Although a variety of TFDs have been proposed, most of their designs were targeted towards obtaining good visualization and limited work is available for characterization applications. In this work, the characteristics of the ambiguity domain (AD) is suitably exploited to obtain a novel automated analysis scheme that preserves the inherent TF connection during Non-Stationary (NS) signal processing. Following this, an energy-based discriminative set of feature vectors for facilitating efficient characterization of the given time-varying input has been proposed. This scheme is motivated by the fact that, although, the interfering (or cross-) terms plague the representation, they carry important signal interaction information, which could be investigated for usability for time-varying signal analysis. Once having assessed the suitability of this domain for NS signal analysis, a new formulation for obtaining AD transformation is introduced. The number theory concepts, specifically the even-ordered Ramanujan Sums (RS) are used to obtain the proposed transform function. A detailed investigation and comparison to the classical approach, on this novel class of functions reveals the many benefits of the RS-modified AD functions: inherent sparsity in representation, dimensionality reduction, and robustness to noise. The next contribution in this work, is the proposal of kernel modifications in AD for obtaining high resolution (and good time localization) distribution. This is motivated by the existing trade-off between TF resolution and interfering term reduction in TF distributions. Here, certain variants of TF kernels are proposed in the AD. In addition, kernels that are derived from the concept of learning machines are introduced for discriminative characterization of NS signals. Following this, two novel AD-based schemes for neurological disorder discrimination using gait and pathological speech detection are introduced. The performance evaluation of these AD-based schemes, using a linear classifier, resulted in a maximum overall classification accuracy of 93.1% and 97.5% for gait and pathological speech applications respectively. The accuracies were obtained after a rigorous leave-one-out technique validation strategy.These results further confirm the potential of the proposed schemes for efficient information extraction for real-life signals.

A Brief Overview on Intelligent Computing-Based Biological Data and Image Analysis

2020 ◽  
pp. 65-89
Author(s):  
Mousomi Roy
Keyword(s):  
Real Life ◽  
Automated Analysis ◽  
Biological Data ◽  
Accurate Diagnosis ◽  
Automated Systems ◽  
Intelligent Computing ◽  
Reasonable Time ◽  
Biological Data Analysis ◽  
Challenging Tasks ◽  
Automated Methods

Biological data analysis is one of the most important and challenging tasks in today's world. Automated analysis of these data is necessary for quick and accurate diagnosis. Intelligent computing-based solutions are highly required to reduce the human intervention as well as time. Artificial intelligence-based methods are frequently used to analyze and mine information from biological data. There are several machine learning-based tools available, using which powerful and intelligent automated systems can be developed. In general, the amount and volume of this kind of data is quite huge and demands sophisticated tools that can efficiently handle this data and produce results within reasonable time by extracting useful information from big data. In this chapter, the authors have made a comprehensive study about different computer-aided automated methods and tools to analyze the different types of biological data. Moreover, this chapter gives an insight about various types of biological data and their real-life applications.

scholarly journals Application of artificial neural networks for automated analysis of cystoscopic images: a review of the current status and future prospects

2020 ◽  
Vol 38 (10) ◽  
pp. 2349-2358 ◽  
Author(s):  
Misgana Negassi ◽  
Rodrigo Suarez-Ibarrola ◽  
Simon Hein ◽  
Arkadiusz Miernik ◽  
Alexander Reiterer
Keyword(s):  
Image Recognition ◽  
English Literature ◽  
Evidence Synthesis ◽  
External Validation ◽  
Real Life ◽  
Automated Analysis ◽  
Current Status ◽  
Video Frame ◽  
Conference Abstract ◽  
Detailed Search

Abstract Background Optimal detection and surveillance of bladder cancer (BCa) rely primarily on the cystoscopic visualization of bladder lesions. AI-assisted cystoscopy may improve image recognition and accelerate data acquisition. Objective To provide a comprehensive review of machine learning (ML), deep learning (DL) and convolutional neural network (CNN) applications in cystoscopic image recognition. Evidence acquisition A detailed search of original articles was performed using the PubMed-MEDLINE database to identify recent English literature relevant to ML, DL and CNN applications in cystoscopic image recognition. Evidence synthesis In total, two articles and one conference abstract were identified addressing the application of AI methods in cystoscopic image recognition. These investigations showed accuracies exceeding 90% for tumor detection; however, future work is necessary to incorporate these methods into AI-aided cystoscopy and compared to other tumor visualization tools. Furthermore, we present results from the RaVeNNA-4pi consortium initiative which has extracted 4200 frames from 62 videos, analyzed them with the U-Net network and achieved an average dice score of 0.67. Improvements in its precision can be achieved by augmenting the video/frame database. Conclusion AI-aided cystoscopy has the potential to outperform urologists at recognizing and classifying bladder lesions. To ensure their real-life implementation, however, these algorithms require external validation to generalize their results across other data sets.

A peek in the micro-sized world: a review of design principles, engineering tools, and applications of engineered microbial community

2020 ◽  
Vol 48 (2) ◽  
pp. 399-409
Author(s):  
Baizhen Gao ◽  
Rushant Sabnis ◽  
Tommaso Costantini ◽  
Robert Jinkerson ◽  
Qing Sun
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Environmental Remediation ◽  
Real Life ◽  
Design Principles ◽  
Microbial Interactions ◽  
Potential Applications ◽  
New Gene ◽  
Global Biogeochemical Cycles ◽  
Synthetic Microbial Communities

Microbial communities drive diverse processes that impact nearly everything on this planet, from global biogeochemical cycles to human health. Harnessing the power of these microorganisms could provide solutions to many of the challenges that face society. However, naturally occurring microbial communities are not optimized for anthropogenic use. An emerging area of research is focusing on engineering synthetic microbial communities to carry out predefined functions. Microbial community engineers are applying design principles like top-down and bottom-up approaches to create synthetic microbial communities having a myriad of real-life applications in health care, disease prevention, and environmental remediation. Multiple genetic engineering tools and delivery approaches can be used to ‘knock-in' new gene functions into microbial communities. A systematic study of the microbial interactions, community assembling principles, and engineering tools are necessary for us to understand the microbial community and to better utilize them. Continued analysis and effort are required to further the current and potential applications of synthetic microbial communities.

The Speech-Language Pathologist in the Public Schools: Preparation for Real Life Practices From the Perspective of a Speech-Language Pathology Program Coordinator

2010 ◽  
Vol 11 (2) ◽  
pp. 60-65
Author(s):  
Francine Wenhardt
Keyword(s):  
Public Schools ◽  
School Setting ◽  
Real Life ◽  
Effective School ◽  
Program Coordinator ◽  
Educational Preparation ◽  
The Public ◽  
Speech Language Pathologist ◽  
New Graduate ◽  
Language Pathology

Abstract The speech-language pathologist (SLP) working in the public schools has a wide variety of tasks. Educational preparation is not all that is needed to be an effective school-based SLP. As a SLP currently working in the capacity of a program coordinator, the author describes the skills required to fulfill the job requirements and responsibilities of the SLP in the school setting and advises the new graduate regarding the interview process and beginning a career in the public schools.

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