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Adaptive information retrieval system based on fuzzy profiling

Obada Alhabashneh, Rahat Iqbal, Faiyaz Doctor, Saad Amin
2015 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
profiles for the tasks, the users and the documents in the data set.  ...  profiles for the tasks, the users and the documents in the data set.  ... 
doi:10.1109/fuzz-ieee.2015.7338012 dblp:conf/fuzzIEEE/AlhabashnehIDA15 fatcat:rj66ried5vgjvnni7mx3gyabwu

Simplifying Access to Large-scale Structured Datasets by Meta-Profiling with Scalable Training Set Enrichment

Sophie Pavia, Rituparna Khan, Anna Pyayt, Michael Gubanov
2022 Proceedings of the 2022 International Conference on Management of Data  
It helps data scientists and end users get access to all relevant topical tables, even in ultra large-scale datasets such as WDC, which would be very difficult or impossible otherwise [25] .  ...  Here we demonstrate our scalable Meta-data profiler, capable of constructing a standardized interface to a topic of interest in large-scale structured datasets using Deep-Learning and our new unsupervised  ...  In addition to the profiles from Figure 1 , it illustrates Songs profiles extracted from YAGO and DBPedia Knowledge Bases.  ... 
doi:10.1145/3514221.3520156 fatcat:azyvzx5ayndpxnlsfqjkui2kyq

A Data-Driven Scalable Method for Profiling and Dynamic Analysis of Shared Mobility Solutions

Bogdan Toader, Assaad Moawad, Thomas Hartmann, Francesco Viti, Zhuping Zhou
2021 Journal of Advanced Transportation  
In this paper, a scalable method for dynamic profiling is introduced, which allows the extraction of users' travel behaviour and valuable knowledge about visited locations, using only geolocation data  ...  At the same time, it challenges analytics platforms to discover knowledge from data in motion (i.e., the analytics occur in real time as the event happens), extract travel habits, and provide reliable  ...  In order to make use of collected data for large-scale mobility sharing services, users' travel behaviour and preferences must be extracted. e very first step in this process is the extraction of the duration  ... 
doi:10.1155/2021/5943567 fatcat:s6be5brykfeslpqdzu6g7aveee

Multi-attribute identity resolution for online social network

Shalini Yadav, Adwitiya Sinha, Pawan Kumar
2019 SN Applied Sciences  
Our research is focused on developing a novel identity resolution framework for detecting redundant user profiles on Twitter social media constructed with nodes, ranging from small-scale to massive-scale  ...  Our proposed solution extracts Twitter user profiles with various attributes, for instance, first name, last name, username, user id, tag line, location, language, profile URL and tweets.  ...  well as a large-scale user-centric Twitter social network.  ... 
doi:10.1007/s42452-019-1701-z fatcat:ftjqtwiczzgxfgtbfa6tyoc5pu

PROFILING AND TRACKING A CYBERLOCKER LINK SHARER IN A PUBLIC WEB FORUM [chapter]

Xiao-Xi Fan, Kam-Pui Chow, Fei Xu
2015 IFIP Advances in Information and Communication Technology  
Multidimensional scaling analysis and agglomerative hierarchical clustering analysis are performed on user profiles to yield clusters of forum users with similar sharing characteristics.  ...  Since cyberlocker users upload and share links via third-party sites, it is possible to harvest cyberlocker-related data from these sites and connect the data to specific users.  ...  During the data collection process, web crawlers extracted data from the nine web forums presented in Table 1 .  ... 
doi:10.1007/978-3-319-24123-4_6 fatcat:oqfu2tnfpvfrvepmflldzv7my4

Automatically Infer Human Traits and Behavior from Social Media Data [article]

Shimei Pan, Tao Ding
2018 arXiv   pre-print
In this paper, we survey the recent work on applying machine learning to infer human traits and behavior from social media data. We will also point out several future research directions.  ...  Given the complexity of human minds and their behavioral flexibility, it requires sophisticated data analysis to sift through a large amount of human behavioral evidence to model human minds and to predict  ...  at a large scale. (2) unstructured and high dimensional user data.  ... 
arXiv:1804.04191v1 fatcat:y6j4haj6cfh2dbjpvmj6hxazc4

Pinned it! A Large Scale Study of the Pinterest Network

Sudip Mittal, Neha Gupta, Prateek Dewan, Ponnurangam Kumaraguru
2014 Proceedings of the 1st IKDD Conference on Data Sciences - CoDS '14  
We characterized Pinterest on the basis of large scale crawls of 3.3 million user profiles, and 58.8 million pins.  ...  We then tried to predict gender of American users based on a set of profile, network, and content features, and achieved an accuracy of 73.17% with a J48 Decision Tree classifier.  ...  from users' profiles.  ... 
doi:10.1145/2567688.2567692 dblp:conf/cods/MittalGDK14 fatcat:etolhq7safdf7cyv2i33v6boke

A feasible route for the design and manufacture of customised respiratory protection through digital facial capture

Luke N. Carter, Caroline A. Reed, Alexander P. Morrell, Anthony K. H. Fong, Rayyan Chowdhury, Ewan Miller, Federico Alberini, Balvinder Khambay, Shivana Anand, Liam M. Grover, Trevor Coward, Owen Addison (+1 others)
2021 Scientific Reports  
Current devices suffer from high fit-failure rates leaving significant proportions of users exposed to risk of viral infection.  ...  These two approaches allow for large scale manufacture of a limited number of design variations, currently nine through the semi-customised approach, whilst ensuring effective device fit.  ...  Facial parameter extraction. Extracting key parameters from facial scan data allows for rapid evaluation of each large and cumbersome scan dataset.  ... 
doi:10.1038/s41598-021-00341-3 pmid:34728650 pmcid:PMC8563770 fatcat:cpnptsun7rdvbk4effvmfgbtem

Automatic Personality and Interaction Style Recognition from Facebook Profile Pictures

Fabio Celli, Elia Bruni, Bruno Lepri
2014 Proceedings of the ACM International Conference on Multimedia - MM '14  
Then, we exploited a bag-of-visualwords technique to extract features from pictures.  ...  In this paper, we address the issue of personality and interaction style recognition from profile pictures in Facebook.  ...  Our paper also uses data from a social network; however it adopts a novel approach using visual features extracted from Facebook profile pictures in order to classify subjects' traits.  ... 
doi:10.1145/2647868.2654977 dblp:conf/mm/CelliBL14 fatcat:nvtobfz6c5fx7kgh32izoeyyoa

Building Context-Rich Mobile Cloud Services For Mobile Cloud Applications

Aleksandar Karadimce
2015 Zenodo  
Developers will use these context-rich support services as building blocks to realize a large class of basic mobile cloud services or mCloud services in short.  ...  Therefore, mobile applications will have to become more personalized, context aware, and able to recognize not only the location of the user, but also their cognitive preferences.  ...  The cloud-based data mining services need to scale from the large data sets the services that should extract the user important social behaviour information.  ... 
doi:10.5281/zenodo.33143 fatcat:ah2mckho2zgnncjqz56xjjmvyy

ReportAGE: Automatically extracting the exact age of Twitter users based on self-reports in tweets

Ari Z. Klein, Arjun Magge, Graciela Gonzalez-Hernandez, Carla Pegoraro
2022 PLoS ONE  
Scaling the detection of exact age to this large number of users can advance the utility of social media data for research applications that do not align with the predefined age groupings of extant binary  ...  Advancing the utility of social media data for research applications requires methods for automatically detecting demographic information about social media study populations, including users' age.  ...  Acknowledgments The authors would like to thank Karen O'Connor, Alexis Upshur, Isaac Valderrabano, and Saahithi Mallapragada for contributing to annotating the tweets; Jonathan Avila for contributing to the age extraction  ... 
doi:10.1371/journal.pone.0262087 pmid:35077484 pmcid:PMC8789116 fatcat:wxtyzirqbfavrerqmiulxwvvxi

Big Data Analytics Model for Distributed Document Using Hybrid Optimization with K-Means Clustering

Kapil Sharma, Satish Saini, Shailja Sharma, Hardeep Singh Kang, Mohamed Bouye, Daniel Krah
2022 Wireless Communications and Mobile Computing  
Clustering, also known as unsupervised learning, is one of the most significant topics of machine learning because it divides data into groups based on similarity with the aim of extracting or summarizing  ...  Furthermore, it is not practicable to consolidate all of the papers from numerous locations into a single area for processing.  ...  Large-scale data processing, as well as analyzing and extracting information from it, has long been a popular topic of discussion.  ... 
doi:10.1155/2022/5807690 doaj:856c6297b9aa4cfb8ffb74a8ba866757 fatcat:g4oew3ccgjfjpkoyhbzrl4rdku

Online Social Network Profile Linkage [chapter]

Haochen Zhang, Min-Yen Kan, Yiqun Liu, Shaoping Ma
2014 Lecture Notes in Computer Science  
We evaluate our approaches on real-world, large-scale datasets obtained from Twitter and Linkedin.  ...  To enable this, we explore a probabilistic approach that uses a domain-specific prior knowledge to address this problem of online social network user profile linkage.  ...  Approach Given the large-scale and reliance on external data, our OPL ("Online Profile Linkage") approach to profile linkage must consider computation cost at the core.  ... 
doi:10.1007/978-3-319-12844-3_17 fatcat:4iu6d7p6snbclpftipdtgrviea

Information Filtering and Automatic Keyword Identification by Artificial Neural Networks

Zvi Boger, Tsvi Kuflik, Bracha Shapira, Peretz Shoval
2000 European Conference on Information Systems  
Information filtering (IF) systems usually filter data items by correlating a vector of terms (keywords) that represent the user profile with similar vectors of terms that represent the data items (e.g  ...  In this study we train a large-scale ANN-based filter which uses meaningful terms in the same database of email messages as input, and use it to predict the relevancy of those messages.  ...  The PCA-CG training algorithm [17] can easily train large scale ANN models, as it pre-computes non-random initial connection weights from the manipulation of training data sets.  ... 
dblp:conf/ecis/BogerKSS00 fatcat:tllwtk2brzdidipfez5m4vdto4

PolyFuse: A Large-Scale Hybrid Data Fusion System

Michael Gubanov
2017 2017 IEEE 33rd International Conference on Data Engineering (ICDE)  
APPLICATIONS First, we demonstrate the large-scale extraction of several semantic profiles from Web45M using the definitions from Section 2.  ...  Section 2 defines the semantic profile and describes the algorithms to extract semantic profiles. Section 3 describes a large-scale storage engines used to run the experiments.  ... 
doi:10.1109/icde.2017.230 dblp:conf/icde/Gubanov17 fatcat:vi3bzo3i6jbzhnjopapeszkqye
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