scholarly journals Incorporating Contextual Information into Personalized Mobile Applications Recommendation

2021 ◽  
Author(s):  
Ke Zhu ◽  
Yingyuan Xiao ◽  
Wenguang Zheng ◽  
Xu Jiao ◽  
Chenchen Sun ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Large Scale ◽  
Contextual Information ◽  
Mobile App ◽  
Mobile Internet ◽  
Current Time ◽  
Spatiotemporal Information ◽  
Data Overload ◽  
Effect Experiment ◽  
Contextual Characteristic

Abstract With the rise of the mobile internet, the number of mobile applications (apps) has shown explosive growth, which directly leads to the apps data overload. Currently, the recommender system has become the most effective method to solve the app data overload. App has the functional exclusiveness feature, which means the target users will not reuse apps with the same function in a certain spatiotemporal information. Most of the existing recommended methods for apps ignore the functional exclusiveness feature which makes it difficult to further improve the recommendation performance of the app recommendation. To solve this problem, we aim to improve the app recommendation performance, and propose a Personalized Context-aware Mobile App Recommendation Approach, called PCMARA. PCMARA comprehensively considers the user and app contextual information, which can mine the users app usage preference effectively. Specifically, (1) PCMARA explores the contextual characteristic of app, and constructs the app contextual factors for app which represent the function of app. (2) For the app functional exclusiveness problem, PCMARA leverages the app contextual factor to design a novel app similarity model, which enable to effectively eliminate this problem. (3) PCMARA considers the contextual information of users and apps to generates a recommendation list for target users based on the target users' current time and location. We applied the PCMARA to a real-world dataset and conducted a large-scale recommendation effect experiment. The experimental results show that the recommendation effect of PCMARA is satisfactory.

scholarly journals Mobile technologies in the professional training of students of economic specialties

2019 ◽  
Vol 7 (1) ◽  
pp. 43-53
Author(s):  
Viacheslav Osadchyi
Keyword(s):  
Mobile Communications ◽  
Mobile Applications ◽  
Professional Training ◽  
Mobile App ◽  
Educational Process ◽  
Mobile Internet ◽  
Mobile Technologies ◽  
Professional Activities ◽  
Modern Information Technology ◽  
Financial Monitoring

Representatives of economic specialties must have the skills to use modern information technology in their professional activities. One of these technologies is mobile, based on the use of mobile devices, services and mobile communications. The purpose of the study is to analyze the opportunities and prospects of mobile learning in the process of professional training of students of economic specialties. In order to study the prospects of introducing mobile technologies into the process of professional training for students of economic specialties in mobile app stores, analysis of applications for the platforms of Google Android and Apple iOS was conducted. Mobile applications have been identified which can be used in the process of training students of economic specialties in terms of content and functionality. They were assigned to the following groups: e-books, directories and dictionaries, news editions, manuals and manuals for economists, simulators of economic processes, appendices for learning a foreign language, question sets and simulators for passing the tests on economics, simulators for work with accounting programs, economic courses, economic calendars, economic calculators, financial monitoring applications, business plans and business ideas, notebooks and planners. As a result of a survey of teachers and students, it was concluded that all interviewed have mobile phones and use mobile Internet. In the educational process, mobile applications use 70% of teachers and 97% of students, including special programs of economics using 50% of teachers and 93% of students. Of the applications of economic orientation, most teachers use manuals for economists (70%) and directories and dictionaries (70%), most students - directories (77%) and training simulators for work with accounting programs (73%). Both lecturers and students indicated that they would like to use mobile applications of economic subjects in professional training. The results of the theoretical analysis and the survey give grounds to assert about the sufficient possibilities of available mobile technologies and the positive attitude towards their use in the professional training of students of economic specialties.

scholarly journals CFDIL: A Context-aware Feature Deep Interaction Learning for  App Recommendation

2021 ◽  
Author(s):  
Qingbo Hao ◽  
Ke Zhu ◽  
Chundong Wang ◽  
Peng Wang ◽  
Xiuliang Mo ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Historical Data ◽  
Contextual Information ◽  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Context Aware ◽  
Interaction Data ◽  
Learning Potential ◽  
The One

Abstract The rapid development of Mobile Internet has spa-wned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users' preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method.

Large-Scale Research via App Stores

2015 ◽  
pp. 269-292
Author(s):  
Matthias Kranz ◽  
Andreas Möller ◽  
Florian Michahelles
Keyword(s):  
Data Collection ◽  
Human Computer Interaction ◽  
Real World ◽  
Mobile Applications ◽  
Large Scale ◽  
Mobile App ◽  
Lessons Learned ◽  
Conducting Research ◽  
Collection Methods

Large-scale research has gained momentum in the context of Mobile Human-Computer Interaction (Mobile HCI), as many aspects of mobile app usage can only be evaluated in the real world. In this chapter, we present findings on the challenges of research in the large via app stores, in conjunction with selected data collection methods (logging, self-reporting) we identified and have proven as useful in our research. As a case study, we investigated the adoption of NFC technology, based on a gamification approach. We therefore describe the development of the game NFC Heroes involving two release cycles. We conclude with lessons learned and provide recommendations for conducting research in the large for mobile applications.

Building a Model-Based GUI Test Automation System for Mobile Applications

2016 ◽  
Vol 26 (09n10) ◽  
pp. 1605-1615 ◽  
Author(s):  
Chuanqi Tao ◽  
Jerry Gao
Keyword(s):  
Mobile Applications ◽  
Large Scale ◽  
Performance Testing ◽  
Mobile App ◽  
Automation System ◽  
Test Automation ◽  
Test Coverage ◽  
Model Based ◽  
Coverage Analysis ◽  
And Performance

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile applications (apps). This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile test automation. Existing mobile application testing approaches mostly concentrate on GUI-based testing, load and performance testing without considering large-scale concurrent mobile app test automation, and model-based test coverage analysis. In this paper, a mobile hierarchical GUI model is proposed to present mobile operation scenario flows and gesture features in a hierarchical manner, in order to facilitate test dependency analysis in test automation. Mobile app test coverage analysis is performed based on GUI ripping models. The paper also presents a developed system that provides a test automation solution using GUI models. Finally, the paper reports a case study to indicate the feasibility and effectiveness of the proposed approach.

scholarly journals Two Stage Continuous Gesture Recognition Based on Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 534
Author(s):  
Huogen Wang
Keyword(s):  
Gesture Recognition ◽  
Large Scale ◽  
Short Term Memory ◽  
Short Term ◽  
Hand Motion ◽  
Spatiotemporal Features ◽  
Spatiotemporal Information ◽  
Video Frames ◽  
Depth Sequences

The paper proposes an effective continuous gesture recognition method, which includes two modules: segmentation and recognition. In the segmentation module, the video frames are divided into gesture frames and transitional frames by using the information of hand motion and appearance, and continuous gesture sequences are segmented into isolated sequences. In the recognition module, our method exploits the spatiotemporal information embedded in RGB and depth sequences. For the RGB modality, our method adopts Convolutional Long Short-Term Memory Networks to learn long-term spatiotemporal features from short-term spatiotemporal features obtained from a 3D convolutional neural network. For the depth modality, our method converts a sequence into Dynamic Images and Motion Dynamic Images through weighted rank pooling and feed them into Convolutional Neural Networks, respectively. Our method has been evaluated on both ChaLearn LAP Large-scale Continuous Gesture Dataset and Montalbano Gesture Dataset and achieved state-of-the-art performance.

Initiating Collaboration among Organ Transplant Professionals through Web Portals and Mobile Applications

2013 ◽  
Vol 18 (2) ◽  
Author(s):  
Susan Alexander ◽  
Haley Hoy ◽  
Manil Maskey ◽  
Helen Conover ◽  
John Gamble ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Healthcare Providers ◽  
Organ Transplant ◽  
Pilot Project ◽  
Mobile App ◽  
Lessons Learned ◽  
Ease Of Use ◽  
Future Directions ◽  
Efficient Access ◽  
Health Related

The knowledge base for healthcare providers working in the field of organ transplantation has grown exponentially. However, the field has no centralized ‘space’ dedicated to efficient access and sharing of information.The ease of use and portability of mobile applications (apps) make them ideal for subspecialists working in complex healthcare environments. In this article, the authors review the literature related to healthcare technology; describe the development of health-related technology; present their mobile app pilot project assessing the effects of a collaborative, mobile app based on a freely available content manage framework; and report their findings. They conclude by sharing both lessons learned while completing this project and future directions.

Assessing Triangulation Across Methodologies, Methods, and Stakeholder Groups: The Joys, Woes, and Politics of Interpreting Convergent and Divergent Data

2018 ◽  
Vol 41 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Rebecca Campbell ◽  
Rachael Goodman-Williams ◽  
Hannah Feeney ◽  
Giannina Fehler-Cabral
Keyword(s):  
Large Scale ◽  
Contextual Information ◽  
Policy Makers ◽  
Specific Data ◽  
Qualitative And Quantitative ◽  
Stakeholder Groups ◽  
Data Points ◽  
Evaluation Project ◽  
Collection Methods

The purpose of this study was to develop triangulation coding methods for a large-scale action research and evaluation project and to examine how practitioners and policy makers interpreted both convergent and divergent data. We created a color-coded system that evaluated the extent of triangulation across methodologies (qualitative and quantitative), data collection methods (observations, interviews, and archival records), and stakeholder groups (five distinct disciplines/organizations). Triangulation was assessed for both specific data points (e.g., a piece of historical/contextual information or qualitative theme) and substantive findings that emanated from further analysis of those data points (e.g., a statistical model or a mechanistic qualitative assertion that links themes). We present five case study examples that explore the complexities of interpreting triangulation data and determining whether data are deemed credible and actionable if not convergent.

Computational modeling of Weibo user influence based on information interactive network

2016 ◽  
Vol 40 (7) ◽  
pp. 867-881 ◽  
Author(s):  
Dingguo Yu ◽  
Nan Chen ◽  
Xu Ran
Keyword(s):  
Social Networks ◽  
Computational Model ◽  
Large Scale ◽  
Mobile Internet ◽  
Activity Level ◽  
Message Content ◽  
Data Set ◽  
Sina Weibo ◽  
Content Type ◽  
User Influence

Purpose With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users. Findings Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter. Originality/value This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.

scholarly journals Cloud Update of Tiled Evidential Occupancy Grid Maps for the Multi-Vehicle Mapping

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4119 ◽  
Author(s):  
Kichun Jo ◽  
Sungjin Cho ◽  
Chansoo Kim ◽  
Paulo Resende ◽  
Benazouz Bradai ◽  
...  
Keyword(s):  
Large Scale ◽  
Cloud Service ◽  
Mobile Internet ◽  
Intelligent Vehicles ◽  
Motion Sensors ◽  
Efficient Operation ◽  
Occupancy Grid ◽  
Occupancy Grid Maps ◽  
Sensor Information ◽  
Evidential Theory

Nowadays, many intelligent vehicles are equipped with various sensors to recognize their surrounding environment and to measure the motion or position of the vehicle. In addition, the number of intelligent vehicles equipped with a mobile Internet modem is increasing. Based on the sensors and Internet connection, the intelligent vehicles are able to share the sensor information with other vehicles via a cloud service. The sensor information sharing via the cloud service promises to improve the safe and efficient operation of the multiple intelligent vehicles. This paper presents a cloud update framework of occupancy grid maps for multiple intelligent vehicles in a large-scale environment. An evidential theory is applied to create the occupancy grid maps to address sensor disturbance such as measurement noise, occlusion and dynamic objects. Multiple vehicles equipped with LiDARs, motion sensors, and a low-cost GPS receiver create the evidential occupancy grid map (EOGM) for their passing trajectory based on GraphSLAM. A geodetic quad-tree tile system is applied to manage the EOGM, which provides a common tiling format to cover the large-scale environment. The created EOGM tiles are uploaded to EOGM cloud and merged with old EOGM tiles in the cloud using Dempster combination of evidential theory. Experiments were performed to evaluate the multiple EOGM mapping and the cloud update framework for large-scale road environment.

scholarly journals Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2868
Author(s):  
Wenxuan Zhao ◽  
Yaqin Zhao ◽  
Liqi Feng ◽  
Jiaxi Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real World ◽  
Large Scale ◽  
Learning Strategy ◽  
Contextual Information ◽  
Small Scale ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Real World Datasets

The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure that adopts a serial strategy of two pruned Unet++ blocks based on residual connection. Compared with the simple Encoder–Decoder structure, the serial Unet++ module can better use the features extracted by encoders and promote contextual information fusion in different resolutions. In addition, we take some improvement measures to the Unet++ module, such as pruning, introducing the convolutional module with ResNet structure, and a residual learning strategy. Thus, the serial Unet++ module can generate more realistic images with less color distortion. Furthermore, following the serial Unet++ blocks, an attention mechanism is introduced to pay different attention to haze regions with different concentrations by learning weights in the spatial domain and channel domain. Experiments are conducted on two representative datasets: the large-scale synthetic dataset RESIDE and the small-scale real-world datasets I-HAZY and O-HAZY. The experimental results show that the proposed dehazing network is not only comparable to state-of-the-art methods for the RESIDE synthetic datasets, but also surpasses them by a very large margin for the I-HAZY and O-HAZY real-world dataset.

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