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IEEE International Conference on Multimedia big data (bigMM)

作者： Alfredo Cuzzocrea The Big Data Engineering and Analytics Laboratory University of Calabria Rende Italy Dept. of Computer Science University of Paris City Paris France

This paper provides a brief overview on the innovative problem of devising and implementing big OLAP data cube compression algorithms in column-oriented Cloud/Edge data infrastructures, an emerging need for next-gener...

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Comparative Analysis of Context Based Classification of Twitter 4

Comparative Analysis of Context Based Classification of Twit...

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4th International Conference on Advances in Computing, Communication and Automation, ICACCA 2018

作者： Tiwana, Muhammad Salman Javeed, Farooq Lali, Muhammad Ikramullah Dar, Hafsa Bilal, Muhammad Dept. of CS and IT University of Sargodha Sargodha Pakistan Dept. of Computer Science University of Gujrat Gujrat Pakistan Dept. of Software Engineering University of Gujrat Gujrat Pakistan School of Computing and IT Taylor's University Subang Jaya Malaysia Centre for Data Science and Analytics Taylor's University Subang Jaya Malaysia

ISBN: (纸本)9781538671672

Social media offers online data source where internet users' share their opinions, views and discuss any news or events that happen around the world. Twitter is a popular social media which effectively takes part in any mega event and is used before, during and after live events. Some tweets become trend based on their popularity. The trending tweets are classified by twitter into categories like: Sports (S), Entertainment (E), Politics (P), and Technology (T) which are used in our work. We have used context based meanings to further improve the trending tweet classification results. In this article, we have verified the twitter's automated classification results with manual classification by experts. Furthermore, we compared the classification results with SVM, Linear Regression, Logistic Regression and Naive Bayes implemented in Weka tool. We found that twitter's classifier works with almost 89% accuracy in our dataset. This work would contribute in improving misclassification of data of different categories on social media. © 2018 IEEE.

关键词： Classification (of information)

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Reputation (In)dependence in ranking systems: Demographics influence over output disparities

arXiv

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arXiv 2020年

作者： Ramos, Guilherme Boratto, Ludovico Dept. of Electrical and Computer Engineering Faculty of Engineering University of Porto Portugal Data Science and Big Data Analytics EURECAT Centre Tecnológic de Catalunya Barcelona Spain

Recent literature on ranking systems (RS) has considered users' exposure when they are the object of the ranking. Although items are the object of reputation-based RS, users have a central role also in this class of algorithms. Indeed, when ranking the items, user preferences are weighted by how relevant this user is in the platform (i.e., their reputation). In this paper, we formulate the concept of disparate reputation (DR) and study if users characterized by sensitive attributes systematically get a lower reputation, leading to a final ranking that reflects less their preferences. We consider two demographic attributes, i.e., gender and age, and show that DR systematically occurs. Then, we propose mitigation, which ensures that reputation is independent of the users' sensitive attributes. Experiments on real-world data show that our approach can overcome DR and also improve ranking effectiveness. Copyright © 2020, The Authors. All rights reserved.

关键词： Population statistics

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Parameter Estimation of Parallel Wiener-Hammerstein Systems by Decoupling their Volterra Representations ⁎

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IFAC-PapersOnLine 2021年第7期54卷 457-462页

作者： Philippe Dreesen Mariya Ishteva KU Leuven Dept. Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium KU Leuven Dept. Computer Science ADVISE-NUMA Belgium

Nonlinear dynamic systems are often approximated by a Volterra series, which is a generalization of the Taylor series for systems with memory. However, the Volterra series lacks physical interpretation. To take advantage of the Volterra representation while aiming for an interpretable block-oriented model, we establish a link between the Volterra representation and the parallel Wiener-Hammerstein model, based on decoupling of multivariate polynomials. The true link is through a constrained decoupling model with (block-)Toeplitz structure on the factors and sets of identical internal branches. The solution of the modified decoupling problem then reveals directly the parameters of the parallel Wiener-Hammerstein model of the system. However, due to the uniqueness properies of the plain decoupling algorithm, even if the structure is not imposed, the method still leads to the true solution (in the exact case).

关键词： Block structured system identification parallel Wiener-Hammerstein systems Volterra kernels tensors canonical polyadic decomposition decoupling multivariate polynomials

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Predicting Evoked Emotions in Video

Predicting Evoked Emotions in Video

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IEEE International Symposium on Multimedia

作者： Joseph G. Ellis W. Sabrina Lin Ching-Yung Lin Shih-Fu Chang Dept. of Electrical Engineering Columbia University New York Network Science and Big Data Analytics IBM Research Yorktown Heights New York

Understanding how human emotion is evoked from visual content is a task that we as people do every day, but machines have not yet mastered. In this work we address the problem of predicting the intended evoked emotion at given points within movie trailers. Movie Trailers are carefully curated to elicit distinct and specific emotional responses from viewers, and are therefore well-suited for emotion prediction. However, current emotion recognition systems struggle to bridge the "affective gap", which refers to the difficulty in modeling high-level human emotions with low-level audio and visual features. To address this problem, we propose a mid-level concept feature, which is based on detectable movie shot concepts which we believe to be tied closely to emotions. Examples of these concepts are "Fight", "Rock Music", and "Kiss". We also create 2 datasets, the first with shot-level concept annotations for learning our concept detectors, and a separate, second dataset with emotion annotations taken throughout the trailers using the two dimensional arousal and valence model for emotion annotation. We report the performance of our concept detectors, and show that by using the output of these detectors as a mid-level representation for the movie shots we are able to more accurately predict the evoked emotion throughout a trailer than by using low-level features.

关键词： Motion pictures Feature extraction Detectors Visualization Pipelines Image color analysis Histograms

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Simplified Micropayment Mechanism to Eliminate the Risk of Double Payment in E-Commerce

Simplified Micropayment Mechanism to Eliminate the Risk of D...

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Advances in Intelligent Computing and Applications (AICAPS), International Conference on

作者： Narendra Kumar Chahar Krishan Pal Singh Muzzammil Hussain Dept. of Data Science and Analytics Central University of Rajasthan Ajmer India Dept. of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India Dept. of Computer Science and Engineering Central University of Rajasthan Ajmer India

In recent years, the emergence of the Internet and E-commerce has steered significant growth in digital transactions. Businesses today need mobile wallets, credit and debit cards, and e-cash to digitize payments. Digital payment systems are in transition and promise amazing advancements, but they also pose many risks and as the number of online transactions is increasing tremendously, we need a security system that follows all security norms. In this paper, we study digital transaction systems and evaluate various components of E-commerce plat-forms to address the security of these services. We evaluate the attributes that affect the security of digital payment processes and identify several barriers that hinder their performance to propose a simplified payment mechanism for micro-payments that eliminates the double payment problem.

关键词： Micropayments Internet Security Electronic commerce Business

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A quantitative evaluation of techniques for detection of abnormal change events in blogs. 12

A quantitative evaluation of techniques for detection of abn...

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12th ACM/IEEE-CS Joint Conference on Digital Libraries, JCDL '12

作者： Bogen II, Paul L. Furuta, Richard Shipman, Frank Intelligent Computing Research Team Computational Data Analytics Group Oak Ridge National Laboratory Oak Ridge TN United States Center for the Study of Digital Libraries Dept. of Computer Science and Engineering Texas A and M University College Station TX 77843 United States

ISBN: (纸本)9781450311540

While most digital collections have limited forms of change - primarily creation and deletion of additional resources - there exists a class of digital collections that undergoes additional kinds of change. These collections are made up of resources that are distributed across the Internet and brought together into a collection via hyperlinking. Resources in these collections can be expected to change as time goes on. Part of the difficulty in maintaining these collections is determining if a changed page is still a valid member of the collection. Others have tried to address this problem by measuring change and defining a maximum allowed threshold of change, however, these methods treat all change as a potential problem and treat web content as a static document despite its intrinsically dynamic nature. Instead, we approach the significance of change on the web as a normal part of a web document's life-cycle and determine the difference between what a maintainer expects a page to do and what it actually does. In this work we evaluate the different options for extractors and analyzers in order to determine the best options from a suite of techniques. The evaluation used a human-generated ground-truth set of blog changes. The results of this work showed a statistically significant improvement over a range of traditional threshold techniques when applied to our collection of tagged blog changes. © 2012 ACM.

关键词： Pattern recognition

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Dependability Issues of Android Games: A First Look via Software Analysis

Dependability Issues of Android Games: A First Look via Soft...

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2015 IEEE Symposium on Service-Oriented System engineering

作者： Jiaojiao Fu Yangfan Zhang Yu Kang School of Computer Science Fudan University Shenzhen Key Laboratory of Rich Media Big Data Analytics and Applications The Chinese University of Hong Kong Fudan University Shanghai Shanghai CN Dept. of Computer Science & Engineering The Chinese University of Hong Kong

Smart phones have surged into popularity in recent years, which has dramatically changed the way people live, work, and have fun. Smart phone games are an important type of Smart phone applications, which attract many software developers. However, they still have not caught much research attention in the software dependability community. In this paper, we study the characteristics of over 2000 Android games with software analysis techniques, with a focus on their dependability issues. Our study suggests that a new security paradigm is of great importance to Smart phone games to prevent potential privilege abuse. Moreover, a new set of testing and debugging approaches should be specifically tailored for Smart phone games, since games are becoming more complicated. Our study also reveals that most games are not specifically optimized according to the user pattern of Smart phones. We expect these open problems can bring more attentions to the software dependability community.

关键词： Games Internet Androids Humanoid robots Libraries Java Software

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Sustainable Energy Efficient Routing Protocol using Chimp algorithm and SOM for Underwater Wireless Sensor Networks

Sustainable Energy Efficient Routing Protocol using Chimp al...

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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： T Philip Paul Arunodhayam Akhil Raj Vadde Thimmampali Prasanna Kumar Salaja Silas Shalem Preetham Gandu Chamarti Harshith Kumar Dept. of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Data Analytics Engineering College of Engineering and Computing George Mason University Fairfax VA United States

Underwater Wireless Sensor Networks (UWSNs) are increasingly being used for various applications, such as environmental monitoring, underwater exploration, and oceanographic research. protocols that consume less energy for underwater wireless sensor networks (UWSNs) are still a difficult undertaking because of the harsh conditions present underwater and the restricted battery capacity of the sensor nodes. This research paper proposes an optimized multi-Hop routing protocol using Chimp Optimization and Self-Organizing Map (SOM). The proposed technique is compared with three popular routing protocols - AODV, DSDV, and DSR in terms of network lifetime and packet delivery ratio. Chimp Optimization algorithm as well as SOM are used to improve network performance. The findings indicate that the put-forward technique exceeds the performance of existing routing protocols, as it increases network lifetime, PDR and PLR while decreasing packet loss ratio. The network lifetime of the put-forward technique is 1039.42 seconds, which is higher than AODV, DSDV, and DSR. Similarly, the PDR of the proposed technique is 97.38%, which is comparatively higher than the three routing protocols. Moreover, the PLR of the proposed technique is 2.62%, which is much lower than AODV, DSDV, and DSR. The proposed technique based on Chimp Optimization and SOM can effectively optimize the multi-Hop routing protocol in underwater wireless sensor networks to achieve sustainable energy efficiency, thus providing important insights for researchers and practitioners to enhance the performance of underwater wireless sensor networks.

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Ocean surface current prediction based on HF radar observations using trajectory-oriented association rule mining

Ocean surface current prediction based on HF radar observati...

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IEEE International Conference on big data

作者： Kulsawasd Jitkajornwanich Peerapon Vateekul Upa Gupta Teeranai Kormongkolkul Arnon Jirakittayakorn Siam Lawawirojwong Siwapon Srisonphan Dept. of Computer Science King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Dept. of Computer Engineering Chulalongkorn University Bangkok Thailand Chulalongkorn University Big Data Analytics & IoT Center (CUBIC) Dept. of Computer Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand GISTDA Academy Geo-Informatics & Space Technology Development Agency (PO) Bangkok Thailand Dept. of Electrical Engineering Kasetsart University Bangkok Thailand

HF (high frequency) coastal radar system is used to capture the surface current behavior - in terms of velocity and direction - in the ocean near the coast. 18 HF coastal radar stations were implemented along the Gulf of Thailand in order to monitor for disasters (e.g., Tsunami) as well as relevant risks. The HF systems are also to serve other life-critical applications, such as water quality control and monitoring, chemical spill backtracking, and marine navigation. However, not all the applications can benefit from this near-real-time HF data; some applications in different domains require forecast values. The examples include search-and-rescue system and hazardous materials spill trajectory prediction. Therefore, in this paper, we propose a predictive model for future current data based on historical HF coastal radar data sets, utilizing association rule mining combined with an object dispersion concept. So, the full potential of HF radar systems can be exploited. The spatial and temporal dimensions are taken into account when designing our predictive system, which consists of two phases: ocean surface current track formulation and spatio-temporal association rule mining. The experiments are performed on a two-year HF radar dataset (2014-2015) using Google Cloud Platform. The resulting forecast current values: velocity and direction are then compared with testing datasets (using 10-fold cross validation) of the actual recorded values and evaluated based on percentage accuracy and RMSE, respectively.

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