scholarly journals Interaction techniques on mobile devices for the blind’s language learning

2020 ◽  
Author(s):  
Steina Melie
Keyword(s):  
Language Learning ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Visually Impaired ◽  
The Other ◽  
Interaction Techniques ◽  
Visual Guidance

Electronic aids on mobile devices are starting to spread to the visually impaired including for language learning. However, they may have the disadvantage of only providing information on the vocabularies and sentences, and not on the other language elements. The non-visual guidance assistance techniques developed on mobile phones can provide these missing indications. They are intended to allow a user to determine the contexts in which the language used especially for the information related to the language interaction.

scholarly journals Interaction Techniques on Mobile Devices for the Blind’s Language Learning

2020 ◽  
Author(s):  
Steina Melie
Keyword(s):  
Language Learning ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Visually Impaired ◽  
The Other ◽  
Interaction Techniques ◽  
Visual Guidance

Electronic aids on mobile devices are starting to spread to the visually impaired including for language learning. However, they may have the disadvantage of only providing information on the vocabularies and sentences, and not on the other language elements. The non-visual guidance assistance techniques developed on mobile phones can provide these missing indications. They are intended to allow a user to determine the contexts in which the language used especially for the information related to the language interaction.

A Critical Analysis of the Use of Mobile Devices in the Classroom and Its Implication for Teaching and Learning

2018 ◽  
pp. 225-239
Author(s):  
Theodora Dame Adjin-Tettey ◽  
Vincentia Abui Akrobotu
Keyword(s):  
High School ◽  
Public Schools ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Critical Analysis ◽  
Teaching And Learning ◽  
Academic Work ◽  
The Other ◽  
Ministry Of Education ◽  
Psychological Growth

The use of mobile devices, especially, by teens has been looked at with much apprehension and suspicion with some saying that it can be used to acquire information which can be detrimental to their social and psychological growth. Some teachers complain that it affects teens' studies as these teenagers stay up late in the night browsing, chatting, watching movies and playing games which cause them to sleep in class or pay little attention because of tiredness. In Ghana students in public schools up to Senior High School are not allowed to use personal mobile phones, laptops and other mobile gadgets in school because of implications such as those enumerated above. On the other hand, some, including those in prominent positions in government, have called for a rethink of such a directive by the Ministry of Education. This chapter critically looks into previous literature on the use of mobile devices in the classroom and suggests ways in which it can be effectively used to advance academic work in the classroom.

scholarly journals Mobile Assisted Language Learning (MALL) – A Brief Survey

2018 ◽  
Vol 2 (2) ◽  
pp. 37-45 ◽  
Author(s):  
Maaruf Ali ◽  
Shayma K. Miraz
Keyword(s):  
Mobile Computing ◽  
Language Learning ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Native Speakers ◽  
Mobile Assisted Language Learning ◽  
Research Article ◽  
Linguistic Ability ◽  
Cellular Mobile

This research article presents a brief survey of language learning applications implemented on mobile computing devices, such as cellular mobile phones. The corpus of the literature show the efficacy and effectiveness of using mobile devices to disseminate, train, retain and improve the linguistic ability of non-native speakers.

scholarly journals Multi-Class Image Classification using CNN and Tflite

2020 ◽  
Vol 3 (11) ◽  
pp. 65-68
Author(s):  
Vishal Shah ◽  
Neha Sajnani
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Image Classification ◽  
Mobile Devices ◽  
Mobile Phones ◽  
High Performance ◽  
Vital Role ◽  
The Other ◽  
Different Types

In recent years’ machine learning is playing a vital role in our everyday lifelike, it can help us to route somewhere, find something for what we aren’t aware of, or can schedule appointments in seconds. Looking at the other side of the coin besides machine learning Mobile phones are equivocating and competing in the same field. If we take an optimistic view, by applying machine learning in our mobile devices, we can make our lives better and even move society forward. Image Classification is the most common and trending topic of machine learning. Among several different types of models in deep learning, Convolutional Neural Networks (CNN’s) have intimated high performance on image classification which are made out of various handling layers to gain proficiency with the portrayals of information with numerous unique levels, are the best AI models as of late. Here, we have trained a simple CNN and completed the experiments on the dataset called Fashion Mnist and Flower Recognition, and also analyzed the techniques of integrating the trained model in the Android platform.

scholarly journals On the Use of Mobile Devices as Controllers for First-Person Navigation in Public Installations

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 238 ◽  
Author(s):  
Spyros Vosinakis ◽  
Anna Gardeli
Keyword(s):  
Mobile Devices ◽  
Degrees Of Freedom ◽  
User Preferences ◽  
The Other ◽  
Microsoft Kinect ◽  
Motion Sensors ◽  
Interaction Techniques ◽  
3D Navigation ◽  
Elicitation Study ◽  
3D Content

User navigation in public installations displaying 3D content is mostly supported by mid-air interactions using motion sensors, such as Microsoft Kinect. On the other hand, smartphones have been used as external controllers of large-screen installations or game environments, and they may also be effective in supporting 3D navigations. This paper aims to examine whether a smartphone-based control is a reliable alternative to mid-air interaction for four degrees of freedom (4-DOF) fist-person navigation, and to discover suitable interaction techniques for a smartphone controller. For this purpose, we setup two studies: A comparative study between smartphone-based and Kinect-based navigation, and a gesture elicitation study to collect user preferences and intentions regarding 3D navigation methods using a smartphone. The results of the first study were encouraging, as users with smartphone input performed at least as good as with Kinect and most of them preferred it as a means of control, whilst the second study produced a number of noteworthy results regarding proposed user gestures and their stance towards using a mobile phone for 3D navigation.

scholarly journals Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sujen Man Maharjan ◽  
Anubhuti Poudyal ◽  
Alastair van Heerden ◽  
Prabin Byanjankar ◽  
Ada Thapa ◽  
...  
Keyword(s):  
Data Collection ◽  
Health Services ◽  
Mobile Devices ◽  
Family Involvement ◽  
Mobile Phones ◽  
Young Mothers ◽  
Audio Recording ◽  
Low Resource Settings ◽  
Passive Sensing ◽  
Passive Data

Abstract Background Passive sensor data from mobile devices can shed light on daily activities, social behavior, and maternal-child interactions to improve maternal and child health services including mental healthcare. We assessed feasibility and acceptability of the Sensing Technologies for Maternal Depression Treatment in Low Resource Settings (StandStrong) platform. The StandStrong passive data collection platform was piloted with adolescent and young mothers, including mothers experiencing postpartum depression, in Nepal. Methods Mothers (15–25 years old) with infants (< 12 months old) were recruited in person from vaccination clinics in rural Nepal. They were provided with an Android smartphone and a Bluetooth beacon to collect data in four domains: the mother’s location using the Global Positioning System (GPS), physical activity using the phone’s accelerometer, auditory environment using episodic audio recording on the phone, and mother-infant proximity measured with the Bluetooth beacon attached to the infant’s clothing. Feasibility and acceptability were evaluated based on the amount of passive sensing data collected compared to the total amount that could be collected in a 2-week period. Endline qualitative interviews were conducted to understand mothers’ experiences and perceptions of passive data collection. Results Of the 782 women approached, 320 met eligibility criteria and 38 mothers (11 depressed, 27 non-depressed) were enrolled. 38 mothers (11 depressed, 27 non-depressed) were enrolled. Across all participants, 5,579 of the hour-long data collection windows had at least one audio recording [mean (M) = 57.4% of the total possible hour-long recording windows per participant; median (Mdn) = 62.6%], 5,001 activity readings (M = 50.6%; Mdn = 63.2%), 4,168 proximity readings (M = 41.1%; Mdn = 47.6%), and 3,482 GPS readings (M = 35.4%; Mdn = 39.2%). Feasibility challenges were phone battery charging, data usage exceeding prepaid limits, and burden of carrying mobile phones. Acceptability challenges were privacy concerns and lack of family involvement. Overall, families’ understanding of passive sensing and families’ awareness of potential benefits to mothers and infants were the major modifiable factors increasing acceptability and reducing gaps in data collection. Conclusion Per sensor type, approximately half of the hour-long collection windows had at least one reading. Feasibility challenges for passive sensing on mobile devices can be addressed by providing alternative phone charging options, reverse billing for the app, and replacing mobile phones with smartwatches. Enhancing acceptability will require greater family involvement and improved communication regarding benefits of passive sensing for psychological interventions and other health services. Registration International Registered Report Identifier (IRRID): DERR1-10.2196/14734

scholarly journals Research on the Use of Mobile Devices and Headphones on Pedestrian Crossings—Pilot Case Study from Slovakia

Safety ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 17
Author(s):  
Miroslava Mikusova ◽  
Joanna Wachnicka ◽  
Joanna Zukowska
Keyword(s):  
Mobile Phone ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Slovak Republic ◽  
Pedestrian Behavior ◽  
The Road ◽  
Phone Calls ◽  
Lack Of Information ◽  
Interaction Factor ◽  
Future Work

The topic of the use of mobile devices and headphones on pedestrian crossings is much less explored in comparison to the use of the mobile phone while driving. Recent years have seen many discussions on this issue, especially in foreign countries. The Slovak Republic, however, has not been giving it enough attention (and it is not mentioned in the National Road Safety Plan for the Slovak Republic from 2011 to 2020). This paper aims to draw attention to this issue. It presents basic outputs of a pilot study on pedestrian safety, with a focus on the use of mobile devices and headphones at selected non-signalized pedestrian crossings in three Slovak cities. Overall, 9% of pedestrians used headphones or mobile devices at observed pedestrian crossings (4% of them used headphones, 1% used headphones and at same time used their mobile phone, 2% made phone calls and 2% used their mobile phones). While these numbers can be considered relatively low, the study proved that during weekdays every 2 min someone was using the crossing without fully focusing on crossing the road safely. Another main finding was that although the safety risk at pedestrian crossings is increased by factors such as rush hour traffic or reduced visibility, pedestrian behavior related to the use of mobile phones and headphones does not change. A safety assessment was also carried out at the crossings. The results show that pedestrian behavior is not affected by the level of safety of the crossing (e.g., visibility of the crossing for drivers). The results of the presented analysis suggest that action is needed to change that. Due to the lack of information about accidents involving pedestrians using mobile phones and headsets when crossing the road, no relevant statistical data could be analyzed. The dataset collected can be used as a basis for further investigation or comparisons with other countries of the relevant indicators. In future work, we would like to include a pedestrian–driver interaction factor focusing on driver speed behavior in relation to pedestrians (who are on or are about to step onto a pedestrian crossing) and identify critical situations caused by improper behavior of drivers and/or pedestrians. This will help to understand speed adjustment problems related to pedestrian crossings.

scholarly journals Comparative Analysis of Geolocation Information through Mobile-Devices under Different COVID-19 Mobility Restriction Patterns in Spain

2021 ◽  
Vol 10 (2) ◽  
pp. 73
Author(s):  
Raquel Pérez-Arnal ◽  
David Conesa ◽  
Sergio Alvarez-Napagao ◽  
Toyotaro Suzumura ◽  
Martí Català ◽  
...  
Keyword(s):  
Mobile Phones ◽  
Human Mobility ◽  
The Other ◽  
Data Sources ◽  
Experimental Setting ◽  
Mobile Data ◽  
Restriction Patterns ◽  
Mobility Data ◽  
The World ◽  
Proper Interpretation

The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.

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