6,892 Hits in 11.2 sec

Generating Pseudo-ground Truth for Predicting New Concepts in Social Streams [chapter]

David Graus, Manos Tsagkias, Lars Buitinck, Maarten de Rijke
2014 Lecture Notes in Computer Science  
We present an unsupervised method for generating pseudo-ground truth for training a named entity recognizer to specifically identify entities that will become concepts in a knowledge base in the setting  ...  of social streams.  ...  Experimental setup In addressing the new concept prediction in document streams problem, we concentrate on developing an unsupervised method for generating pseudo-ground truth for NERC and predicting new  ... 
doi:10.1007/978-3-319-06028-6_24 fatcat:reiyydnqjra43ivhqitwxlsnye

What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper [article]

Ali Ayub, Alan R. Wagner
2020 arXiv   pre-print
This paper presents a computational framework for learning contexts and the social norms present in a context in an online manner on a robot.  ...  Social norms support coordination and cooperation in society. With social robots becoming increasingly involved in our society, they also need to follow the social norms of the society.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  ... 
arXiv:2009.05105v1 fatcat:kjdg5ola5jh3hgax35flpos3e4

Data Mining and Knowledge Discovery [chapter]

Chao Zhang, Jiawei Han
2021 The Urban Book Series  
In this chapter, we present recent developments in data-mining techniques for urban activity modeling, a fundamental task for extracting useful urban knowledge from social-sensing data.  ...  The availability of massive social-sensing data provides a unique opportunity for understanding urban space in a data-driven manner and improving many urban computing applications, ranging from urban planning  ...  We predicted the location at two different granularities: (1) coarse-grained region prediction of the ground-truth region that r falls in; and (2) fine-grained POI prediction of the ground-truth POI  ... 
doi:10.1007/978-981-15-8983-6_42 fatcat:gxnx3jgu4fcqvbieg3ltmdovk4

Hyper-Heuristic Framework for Sequential Semi-Supervised Classification Based on Core Clustering

Ahmed Adnan, Abdullah Muhammed, Abdul Azim Abd Ghani, Azizol Abdullah, Fahrul Hakim
2020 Symmetry  
However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of  ...  Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift.  ...  General Framework E p , E c denote the difference between the class prediction result in one side and the predicted NN or actual ground truth respectively.  ... 
doi:10.3390/sym12081292 fatcat:jim4lasoojcinkbe65iaccqgvq

Predicting Future Rumours

Yumeng Qin, Wurzer Dominik, Cunchen Tang
2018 Chinese journal of electronics  
We introduce a new task called "rumour prediction" that assesses the possibility of a document arriving from a social media stream becoming a rumour in the future.  ...  Our approach to rumour prediction harnesses content based features in combination with novelty based features and pseudo feedback.  ...  [1] states that in the majority of cases, news wires lead social media for reporting news.  ... 
doi:10.1049/cje.2018.03.008 fatcat:mi37zoq3tffspjrtlu7yncmel4

Real-time Adaptive Crawler for Tracking Unfolding Events on Twitter

Asmelash Teka Hadgu, Sallam Abualhaija, Claudia Niederée
2019 International Conference on Information Systems for Crisis Response and Management  
In addition, we present a framework to evaluate real-time adaptive social search algorithms in a reproducible manner, which relies on a semi-automated approach for ground-truth construction.  ...  In this paper, we propose a novel method for social media crawling, which exploits a Bayesian inference framework to keep track of keyword changes over time and uses a counter-stream to gauge the inclusion  ...  Hailu and the anonymous reviewers for their valuable feedback on the manuscript.  ... 
dblp:conf/iscram/HadguAN19 fatcat:2h5czq6sjzgmtokypi3w2u7c5u

Human-in-the-loop Handling of Knowledge Drift

Bontempelli Andrea, Giunchiglia Fausto, Passerini Andrea, Teso Stefano
2021 Zenodo  
The main challenge is that, since the ground-truth concept hierarchy is unobserved, it is hard to tell apart different forms of KD.  ...  For instance, introducing a new is-a relation between two concepts might be confused with individual changes to those concepts, but it is far from equivalent.  ...  ACKNOWLEDGMENTS We are grateful to the anonymous reviewers for helping us to improve the paper.  ... 
doi:10.5281/zenodo.5213713 fatcat:n75bhs7hpbcwli5oy7ytygm5pu

Semi-supervised Drifted Stream Learning with Short Lookback [article]

Weijieying Ren, Pengyang Wang, Xiaolin Li, Charles E. Hughes, Yanjie Fu
2022 arXiv   pre-print
The framework is able to accomplish: 1) robust pseudo-labeling in the generation step; 2) anti-forgetting adaption in the replay step.  ...  In many scenarios, 1) data streams are generated in real time; 2) labeled data are expensive and only limited labels are available in the beginning; 3) real-world data is not always i.i.d. and data drift  ...  Noted that JT is the upper bound of the performance since JT assumes the availability of ground-truth labels across streaming data and restores all these data for training the model.  ... 
arXiv:2205.13066v1 fatcat:xpr4j4xnhrbtnmbc3sryr3zbyu

Fast concept drift detection using unlabeled data

Dan Shang, Guangquan Zhang, Jie Lu
2020 Developments of Artificial Intelligence Technologies in Computation and Robotics  
Streaming data mining is in use today in many industrial applications, but performance of the models is deteriorated by concept drift, especially when true labels are unavailable.  ...  This paper addresses the need of detecting concept drifts under unsupervised situation and proposes the Unsupervised Concept Drift Detection (UCDD) method.  ...  Acknowledgments The work presented in this paper was supported by the Australian Research Council (ARC) under Discovery Project DP190101733.  ... 
doi:10.1142/9789811223334_0017 fatcat:elfrbwreffh2fgfhcfdsl5qure

One-Shot Unsupervised Cross-Domain Detection [article]

Antonio D'Innocente, Francesco Cappio Borlino, Silvia Bucci, Barbara Caputo, Tatiana Tommasi
2020 arXiv   pre-print
Consider for instance the task of monitoring image feeds from social media: as every image is created and uploaded by a different user it belongs to a different target domain that is impossible to foresee  ...  This is a heavy assumption, as often it is not possible to anticipate the domain where a detector will be used, nor to access it in advance for data acquisition.  ...  Social Bikes is our new concept-dataset containing 30 images of scenes with persons/bicycles collected from Twitter, Instagram and Facebook by searching for #bike tags.  ... 
arXiv:2005.11610v1 fatcat:gl6cewohafcolbfuber4buniba

Click-through Prediction for Advertising in Twitter Timeline

Cheng Li, Yue Lu, Qiaozhu Mei, Dong Wang, Sandeep Pandey
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
We present the problem of click-through prediction for advertising in Twitter timeline, which displays a stream of Tweets from accounts a user choose to follow.  ...  This makes the information available for training a machine learning model extremely sparse.  ...  x − ) in imp_map do 3: T t=1 yt/|T |, where yt ∈ {0, 1} is the ground-truth label.  ... 
doi:10.1145/2783258.2788582 dblp:conf/kdd/LiLMWP15 fatcat:jusfgw4f4fbe3oqkc25wcbrw2u

Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media

Calton Pu, Abhijit Suprem, Rodrigo Alves Lima
2020 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)  
Although limited in quantity, the reliable training data from authoritative sources enable the filtering of misinformation as well as capturing truly new information.  ...  A rapidly evolving situation such as the COVID-19 pandemic is a significant challenge for AI/ML models because of its unpredictability.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funding  ... 
doi:10.1109/cogmi50398.2020.00026 fatcat:eczfflucnbhvtgtltjl6gxqdde

Whispers in the dark

Gang Wang, Bolun Wang, Tianyi Wang, Ana Nika, Haitao Zheng, Ben Y. Zhao
2014 Proceedings of the 2014 Conference on Internet Measurement Conference - IMC '14  
Increasing awareness of privacy issues and events such as the Snowden disclosures have led to the rapid growth of a new generation of anonymous social networks and messaging applications.  ...  Social interactions and interpersonal communication has undergone significant changes in recent years.  ...  Acknowledgments We would like to thank our shepherd Alan Mislove and the anonymous reviewers for their comments.  ... 
doi:10.1145/2663716.2663728 dblp:conf/imc/WangWWNZZ14 fatcat:4biviaraknhj5l3s5rjbpbwdgu

Unsupervised Detection of State Changes During Operation of Machine Elements

Hillenbrand Jonas, Fleischer Jürgen
2021 Journal of Machine Engineering  
This paper introduces a new clustering scheme that incorporates iterative cluster retrieval in order to track the clustering results over time.  ...  The results show a general applicability to the unsupervised monitoring of machine elements using the proposed approach.  ...  In the case of data with more dimensions, a new generalized concept for high-dimensional cluster boundaries has to be conceived.  ... 
doi:10.36897/jme/136311 fatcat:iqwbokvslrabjifxlcugkrxhpm

Unsupervised by any other name: Hidden layers of knowledge production in artificial intelligence on social media

Anja Bechmann, Geoffrey C Bowker
2019 Big Data & Society  
The rhetoric is that ensuing predictions work well-with a high degree of autonomy and automation.  ...  Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards  ...  Henrik Pedersen and Anne Henriksen, Judith Gregory for comments on earlier version of the paper, special issue editors, and anonymous reviewers for their insightful suggestions.  ... 
doi:10.1177/2053951718819569 fatcat:zhrazh7e4jh2hcswi3p4skenzq
« Previous Showing results 1 — 15 out of 6,892 results