Donors looking to tech, mobile platforms for giving options

2022 ◽  
Vol 2022 (389) ◽  
pp. 7-8
Keyword(s):  
Mobile Platforms

Using Popular Mobile Devices in Voice Therapy

2013 ◽  
Vol 23 (3) ◽  
pp. 82-87 ◽  
Author(s):  
Eva van Leer
Keyword(s):  
Exercise Behavior ◽  
Voice Therapy ◽  
Pure Oxygen ◽  
Mobile Platforms ◽  
Self Monitoring ◽  
Web Based ◽  
Motivational Messages ◽  
Related Information ◽  
Health Related ◽  
Tailored Approach

Mobile tools are increasingly available to help individuals monitor their progress toward health behavior goals. Commonly known commercial products for health and fitness self-monitoring include wearable devices such as the Fitbit© and Nike + Pedometer© that work independently or in conjunction with mobile platforms (e.g., smartphones, media players) as well as web-based interfaces. These tools track and graph exercise behavior, provide motivational messages, offer health-related information, and allow users to share their accomplishments via social media. Approximately 2 million software programs or “apps” have been designed for mobile platforms (Pure Oxygen Mobile, 2013), many of which are health-related. The development of mobile health devices and applications is advancing so quickly that the Food and Drug Administration issued a Guidance statement with the purpose of defining mobile medical applications and describing a tailored approach to their regulation.

Designing Apps Interoperable and Functional on Multiple Mobile Platforms using Google Environment

2018 ◽  
Vol 2018 (6) ◽  
pp. 115-1-115-11
Author(s):  
Devasena Inupakutika ◽  
Chetan Basutkar ◽  
Sahak Kaghyan ◽  
David Akopian ◽  
Patricia Chalela ◽  
...  
Keyword(s):  
Mobile Platforms

Web Service Implementation in Logistics Company uses JSON Web Token and RC4 Cryptography Algorithm

2020 ◽  
Vol 4 (3) ◽  
pp. 591-600
Author(s):  
Mochammad Rizky Royani ◽  
Arief Wibowo
Keyword(s):  
Web Service ◽  
Data Storage ◽  
Growth Potential ◽  
Easy Access ◽  
Access Time ◽  
Mobile Platforms ◽  
Logistics Service ◽  
Authentication Mechanism ◽  
Cryptographic Algorithm ◽  
Service Application

The development of e-commerce in Indonesia in the last five years has significantly increased the growth for logistics service companies. The Indonesian Logistics and Forwarders Association (ALFI) has predicted the growth potential of the logistics business in Indonesia to reach more than 30% by 2020. One of the efforts of logistics business companies to improve services in the logistics services business competition is to implement web service technology on mobile platforms, to easy access to services for customers. This research aims to build a web service with a RESTful approach. The REST architecture has limitations in the form of no authentication mechanism, so users can access and modify data. To improve its services, JSON Web Token (JWT) technology is needed in the authentication process and security of access rights. In terms of data storage and transmission security, a cryptographic algorithm is also needed to encrypt and maintain confidentiality in the database. RC4 algorithm is a cryptographic algorithm that is famous for its speed in the encoding process. RC4 encryption results are processed with the Base64 Algorithm so that encrypted messages can be stored in a database. The combination of the RC4 method with the Base64 method has strengthened aspects of database security. This research resulted in a prototype application that was built with a combination of web service methods, JWT and cryptographic techniques. The test results show that the web service application at the logistics service company that was created can run well with relatively fast access time, which is an average of 176 ms. With this access time, the process of managing data and information becomes more efficient because before making this application the process of handling a transaction takes up to 20 minutes.

Developing a Kinematic Estimation Model for a Climbing Mobile Robotic Welding System

2010 ◽  
Author(s):  
Aaron T. O’Toole ◽  
Stephen L. Canfield
Keyword(s):  
Kinematic Model ◽  
Robotic Welding ◽  
Estimation Model ◽  
Mobile Platforms ◽  
Low Profile ◽  
Turning Radius ◽  
Equivalence Model ◽  
Kinematic Equivalence ◽  
Welding System ◽  
Robot Platform

Skid steer tracked-based robots are popular due to their mechanical simplicity, zero-turning radius and greater traction. This architecture also has several advantages when employed by mobile platforms designed to climb and navigate ferrous surfaces, such as increased magnet density and low profile (center of gravity). However, creating a kinematic model for localization and motion control of this architecture is complicated due to the fact that tracks necessarily slip and do not roll. Such a model could be based on a heuristic representation, an experimentally-based characterization or a probabilistic form. This paper will extend an experimentally-based kinematic equivalence model to a climbing, track-based robot platform. The model will be adapted to account for the unique mobility characteristics associated with climbing. The accuracy of the model will be evaluated in several representative tasks. Application of this model to a climbing mobile robotic welding system (MRWS) is presented.

Mobile-assisted vocabulary learning: Investigating receptive and productive vocabulary knowledge of Chinese EFL learners

ReCALL ◽  
2021 ◽  
pp. 1-15
Author(s):  
Yan Li ◽  
Christoph A. Hafner
Keyword(s):  
Undergraduate Students ◽  
Vocabulary Learning ◽  
Vocabulary Knowledge ◽  
Mobile Technologies ◽  
Word Knowledge ◽  
Mobile Platforms ◽  
Efl Learners ◽  
Learning Group ◽  
Chinese University ◽  
L2 Vocabulary

Abstract Considerable research has been conducted on the advancement of mobile technologies to facilitate vocabulary learning and acquisition in a second language (L2). However, whether mobile platforms lead to a comprehensive mastery of both receptive and productive vocabulary knowledge has seldom been addressed in previous literature. This study investigated English vocabulary learning from engagement with mobile-based word cards and paper word cards in the context of the Chinese university classroom. A total of 85 undergraduate students were recruited to take part in the study. The students were divided into two groups, a mobile learning group and a paper-based learning group, and tested on two word knowledge components: receptive knowledge of the form–meaning connection and productive knowledge of collocations. Both the digital and non-digital word cards enhanced L2 vocabulary learning, and the results showed that the mobile application (app) promoted greater gains than physical word cards.

scholarly journals A new multi-scale backbone network for object detection based on asymmetric convolutions

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110113
Author(s):  
Xianghua Ma ◽  
Zhenkun Yang
Keyword(s):  
Object Detection ◽  
Image Features ◽  
Detection Accuracy ◽  
Mobile Platforms ◽  
Multi Scale ◽  
Backbone Network ◽  
Aspect Ratios ◽  
Pascal Voc ◽  
Scale Characteristics ◽  
Detection Speed

Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. In order to improve detecting accuracy, it is beneficial to extract complete multi-scale image features in visual cognitive tasks. Asymmetric convolutions have a useful quality, that is, they have different aspect ratios, which can be used to exact image features of objects, especially objects with multi-scale characteristics. In this paper, we exploit three different asymmetric convolutions in parallel and propose a new multi-scale asymmetric convolution unit, namely MAC block to enhance multi-scale representation ability of CNNs. In addition, MAC block can adaptively merge the features with different scales by allocating learnable weighted parameters to three different asymmetric convolution branches. The proposed MAC blocks can be inserted into the state-of-the-art backbone such as ResNet-50 to form a new multi-scale backbone network of object detectors. To evaluate the performance of MAC block, we conduct experiments on CIFAR-100, PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO 2014 datasets. Experimental results show that the detection precision can be greatly improved while a fast detection speed is guaranteed as well.

A distributional semantics-based information retrieval framework for online social networks

2021 ◽  
pp. 1-11
Author(s):  
V.S. Anoop ◽  
P. Deepak ◽  
S. Asharaf
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Online Social Networks ◽  
Latent Dirichlet Allocation ◽  
Relevant Information ◽  
Distributional Semantics ◽  
Scalable Algorithms ◽  
Mobile Platforms ◽  
Cancer Support ◽  
Efficient Extraction

Online social networks are considered to be one of the most disruptive platforms where people communicate with each other on any topic ranging from funny cat videos to cancer support. The widespread diffusion of mobile platforms such as smart-phones causes the number of messages shared in such platforms to grow heavily, thus more intelligent and scalable algorithms are needed for efficient extraction of useful information. This paper proposes a method for retrieving relevant information from social network messages using a distributional semantics-based framework powered by topic modeling. The proposed framework combines the Latent Dirichlet Allocation and distributional representation of phrases (Phrase2Vec) for effective information retrieval from online social networks. Extensive and systematic experiments on messages collected from Twitter (tweets) show this approach outperforms some state-of-the-art approaches in terms of precision and accuracy and better information retrieval is possible using the proposed method.

scholarly journals Digitalisation of Teaching and Learning as a Tool for Increasing Students’ Satisfaction and Educational Efficiency: Using Smart Platforms in EFL

2021 ◽  
Vol 13 (9) ◽  
pp. 4892
Author(s):  
Sandra Stefanovic ◽  
Elena Klochkova
Keyword(s):  
Foreign Language ◽  
Learning Process ◽  
Teaching And Learning ◽  
English Language ◽  
Learning Processes ◽  
Application Development ◽  
Mobile Platforms ◽  
Digital Platforms ◽  
Motivation For Learning ◽  
Study Programs

This manuscript aims to present possibilities for developing mobile and smart platforms and systems in teaching and learning the English language for engineering professionals in different engineering study programs. Foreign language teaching and learning processes are based on traditional methods, while in engineering and technical sciences, teaching and learning processes include different digital platforms. Therefore, the following hypotheses were stated. (H1) It is possible to develop a software solution for mobile platforms that can have a higher level of interactivity, and it may lead to better learning outcomes, especially in the field of adopting engineering vocabulary. (H2) Implementation of the developed solution increases motivation for learning and leads to a higher level of satisfaction with the learning process as a part of the quality of life. (H3) Students who have digital and mobile platforms in the learning process could have higher achievement values. This manuscript presents software application development and its implementation in teaching English as a foreign language for engineering and technical study programs on the bachelor level. Initial results in implementation and satisfaction of end users point to the justification of implementing such solutions.

Enabling Real-time Sign Language Translation on Mobile Platforms with On-board Depth Cameras

2021 ◽  
Vol 5 (2) ◽  
pp. 1-30
Author(s):  
HyeonJung Park ◽  
Youngki Lee ◽  
JeongGil Ko
Keyword(s):  
Real Time ◽  
Sign Language ◽  
Data Augmentation ◽  
Language Translation ◽  
Mobile Platforms ◽  
Depth Cameras ◽  
Language Data ◽  
In The Wild ◽  
Environmental Robustness ◽  
Cloud Servers

In this work we present SUGO, a depth video-based system for translating sign language to text using a smartphone's front camera. While exploiting depth-only videos offer benefits such as being less privacy-invasive compared to using RGB videos, it introduces new challenges which include dealing with low video resolutions and the sensors' sensitiveness towards user motion. We overcome these challenges by diversifying our sign language video dataset to be robust to various usage scenarios via data augmentation and design a set of schemes to emphasize human gestures from the input images for effective sign detection. The inference engine of SUGO is based on a 3-dimensional convolutional neural network (3DCNN) to classify a sequence of video frames as a pre-trained word. Furthermore, the overall operations are designed to be light-weight so that sign language translation takes place in real-time using only the resources available on a smartphone, with no help from cloud servers nor external sensing components. Specifically, to train and test SUGO, we collect sign language data from 20 individuals for 50 Korean Sign Language words, summing up to a dataset of ~5,000 sign gestures and collect additional in-the-wild data to evaluate the performance of SUGO in real-world usage scenarios with different lighting conditions and daily activities. Comprehensively, our extensive evaluations show that SUGO can properly classify sign words with an accuracy of up to 91% and also suggest that the system is suitable (in terms of resource usage, latency, and environmental robustness) to enable a fully mobile solution for sign language translation.

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