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App Download Forecasting: An Evolutionary Hierarchical Competition Approach

Yingzi Wang, Nicholas Jing Yuan, Yu Sun, Chuan Qin, Xing Xie
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
However, most existing approaches fail to model the evolutionary competition among products and lack the capability to organically reflect multi-level competition analysis in sales forecasting.  ...  Extensive experiments using a real-world app download dataset show that EHCM outperforms state-of-the-art methods in various forecasting granularities.  ...  Therefore, we propose an Evolutionary Hierarchical Competition Model (EHCM) to model the time-evolving hierarchical competition among products and to provide more accurate product sales forecasting.  ... 
doi:10.24963/ijcai.2017/415 dblp:conf/ijcai/WangYSQ017 fatcat:7wqbigk62nfjzauo5ja4bybxjy

Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data: A Review

Bin Guo, Yi Ouyang, Tong Guo, Longbing Cao, Zhiwen Yu
2019 IEEE Access  
Finally, an evolvable app ecosystem architecture based on heterogeneous crowdsourced data is presented.  ...  To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback  ...  [63] consider the competition among different mobile apps, and they proposed an evolutionary hierarchical competition model to forecast the app downloads. Lu et al.  ... 
doi:10.1109/access.2019.2918325 fatcat:de763kc4qbdy5ijo55jxyhzgt4

The Third Mission of the university: A systematic literature review on potentials and constraints

Lorenzo Compagnucci, Francesca Spigarelli
2020 Technological forecasting & social change  
The authors suggested that TM has at least three dimensions: (i) a non-profit -social -approach; (ii) an entrepreneur focus; and (iii) an innovative approximation.  ...  Aragonés-Beltrán et al. (2017) suggested an alternative approach to the strategic planning process of KT activities.  ... 
doi:10.1016/j.techfore.2020.120284 fatcat:5fuz5lq23japvfgox2ar3xb474

Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A Survey

Bin Guo, Yan Liu, Yi Ouyang, Vincent W. Zheng, Daqing Zhang, Zhiwen Yu
2019 IEEE Access  
and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis.  ...  traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting  ...  [137] propose the evolutionary hierarchical competition model (EHCM) to model the time-evolving hierarchical competition among products, and it provides more accurate product sales forecasting.  ... 
doi:10.1109/access.2019.2901027 fatcat:a5vz6vl7urckpdsreplkvjalea

Smartphone and Tablet Application (App) Life Cycle Characterization via Apple App Store Rank

Han Jia, Chun Guo, Xiaozhong Liu
2020 Data and Information Management  
an App.  ...  Instead of trying to utilize often-unavailable sales or download volume data, we use open-access App daily download rankings as an indicator to characterize the normalized dynamic market popularity of  ...  Unlike other regression studies in the social sciences, we used an automatic approach to collect a large number of Apps in our database.  ... 
doi:10.2478/dim-2020-0002 fatcat:ikf6duyr4vd5hb743go6cmnfea

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Tong Li, Tong Xia, Huandong Wang, Zhen Tu, Sasu Tarkoma, Zhu Han, Pan Hui
2022 IEEE Communications Surveys and Tutorials  
App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones.  ...  We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets.  ...  [79] proposed an evolutionary hierarchical competition model (EHCM) to predict app downloads by using a three-level hierarchy structure to model app adoption.  ... 
doi:10.1109/comst.2022.3163176 fatcat:yj656343ovevdldtiw6vf254ue

Electric Kickboard Demand Prediction in Spatiotemporal Dimension Using Clustering-Aided Bagging Regressor

Prince Waqas Khan, Se-Joon Park, Sang-Joon Lee, Yung-Cheol Byun, Mohammad Miralinaghi
2022 Journal of Advanced Transportation  
In order to gain a competitive edge and to provide quality service to customers, it is essential to properly deploy rental electric kickboards (e-kickboards) at the time and place customers want.  ...  We have compared our proposed approach with other state-of-the-art models, and we have also compared our model with different other combinations of bagging regressors.  ...  We have achieved an RMSE of 24.67 using our proposed approach. Table 3 shows a comparison of the proposed approach with different individual models.  ... 
doi:10.1155/2022/8062932 fatcat:ynebfss23nbhlifuot7s3s44gu

HAWK: Rapid Android Malware Detection through Heterogeneous Graph Attention Networks [article]

Yiming Hei, Renyu Yang, Hao Peng, Lihong Wang, Xiaolin Xu, Jianwei Liu, Hong Liu, Jie Xu, Lichao Sun
2021 arXiv   pre-print
out-of-sample application, with the accelerated training time of 50x faster than the existing approach.  ...  To address this issue, we present HAWK, a new malware detection framework for evolutionary Android applications.  ...  DroidEvolver is also based on feature engineering and updates its model in an online manner according to out-of-sample Apps, leading to a competitive classification accuracy.  ... 
arXiv:2108.07548v1 fatcat:wizwynfcmfhi5fkkll5c4kaof4

Detecting cyber threats through social network analysis: short survey [article]

Lyudmyla Kirichenko, Tamara Radivilova, Anders Carlsson
2018 arXiv   pre-print
The methods of cluster analysis can be divided into two groups: hierarchical and non-hierarchical. Each group includes a variety of approaches and algorithms [64] .  ...  cluster analysis methods, the non-hierarchical ones, including k-means and kmedians algorithms; association rules learning, including a priori algorithm; enumeration methods; evolutionary programming  ... 
arXiv:1805.06680v1 fatcat:iqdtg73oovgaxeupdovfk4valy

Detecting cyber threats through social network analysis: short survey

Lyudmyla Kirichenko, Tamara Radivilova, Carlsson Anders
2017 SocioEconomic Challenges  
The methods of cluster analysis can be divided into two groups: hierarchical and non-hierarchical. Each group includes a variety of approaches and algorithms [64] .  ...  cluster analysis methods, the non-hierarchical ones, including k-means and kmedians algorithms; association rules learning, including a priori algorithm; enumeration methods; evolutionary programming  ... 
doi:10.21272/sec.2017.1-03 fatcat:m7wj7om4o5hthluqblwcpcbmni

Which Open Internet Framework is Best for Mobile App innovation? [article]

Roslyn Layton
2017 Ph.d.-serien for Det Teknisk-Naturvidenskabelige Fakultet, Aalborg Universitet  
With an app, a developer creates an app to focus on a use case, uploads the app to the app marketplace, and then the users download the app.  ...  app downloads, usage, and revenue.  ...  This may explain why the regulator preferred a soft approach to net neutrality with a code of conduct, believing that strict rules could have negative consequences.  ... 
doi:10.5278/vbn.phd.engsci.00181 fatcat:kz5jx2gnvnbthjklxy7gp7jzke

A Review on Big Data: Views, Categories and Aspects

Diwakar Shukla, Abdul Alim
2018 International Journal of Computer Applications  
The problem which appears is how to manage this huge data in a systematic way because users want quick response on web search or on smart phone access using web apps.  ...  It is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that  ...  Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process.  ... 
doi:10.5120/ijca2018916489 fatcat:phs2q4wuhvfqlowhifyky72zbu

Digital Government, Open Architecture, and Innovation: Why Public Sector IT Will Never Be the Same Again

J. Fishenden, M. Thompson
2012 Journal of public administration research and theory  
evolutionary dead end.  ...  by guest on September 7, 2012 http://jpart.oxfordjournals.org/ Downloaded from Table 3 3 Same Aim, Different Models: How NPM and Open Architecture Differ in Their Approach to Disaggregation, Competition  ... 
doi:10.1093/jopart/mus022 fatcat:lbbsrlq6zje5vk5t2cwi2z5yiy

Walled gardens

Nancy Paterson
2012 Proceedings of the 2012 iConference on - iConference '12  
The browser-centric public Web has been giving way to 'apps' and 'walled gardens'.  ...  From a user's perspective: the network can include the activities of an end user, competitive network provider, an independent content/software provider, or the network owner itself.  ...  This MPLS network 'core' is also called an MPLS 'cloud' and consists of routing where directly connected hardware/software (in the form of custom label-switched routers termed 'hierarchical cloud routers  ... 
doi:10.1145/2132176.2132189 dblp:conf/iconference/Paterson12 fatcat:xfa3d2yz2jclxhqdmglgd5oyx4

Cyber Attacks Mitigation: Detecting Malicious Activities in Network Traffic – A Review of Literature

Sangeetha Prabhu, Subramanya Bhat
2020 Zenodo  
Furthermore, as the Internet of Things (IoT) has arisen, the number of Internetconnected devices is increasingly growing and being an easy target of cyber-attacks.  ...  In this literature survey, we are highlighting the work Performed throughout the area of cyber security by various researchers, various types of cyber-attacks and its stages, various approaches to prevent  ...  Stay ahead of Competitors -Implementing Security Strategies in competition puts company competitive. IT Protection System blends with enterprise systems that already exist.  ... 
doi:10.5281/zenodo.3978912 fatcat:u6dcenmygvcjpbtveyc7rbfyl4
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