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Tweets as a vote: Exploring political sentiments on twitter for opinion mining 22nd

Tweets as a vote: Exploring political sentiments on twitter ...

引用

22nd international Symposium on Methodologies for intelligent Systems, ISMIS 2015

作者： Olorunnimbe, Muhammed K. Viktor, Herna L. School of Information Technology and Engineering University of Ottawa OttawaONK1N 6N5 Canada

ISBN: (纸本)9783319252513

Twitter feeds provide data scientists with a large repository for entity based sentiment analysis. Specifically, the tweets of individual users may be used in order to track the ebb and flow of their sentiments and opinions. However, this domain poses a challenge for traditional classifiers, since the vast majority of tweets are unlabeled. Further, tweets arrive at high speeds and in very large volumes. They are also suspect to change over time (so-called concept drift). In this paper, we present the PyStream algorithm that addresses these issues. Our method starts with a small annotated training set and bootstraps the learning process. We employ online analytic processing (OLAP) to aggregate the opinions of the individuals we track, expressed in terms of the votes they would cast in a national election. Our results indicate that we are able to capture the sentiments of individuals as they evolve over time. © Springer-Verlag Berlin Heidelberg 2015.

关键词： Sentiment analysis

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Improving the Ability of Future Engineers by Using Advanced Interactive 3D Techniques in Education 22

Improving the Ability of Future Engineers by Using Advanced ...

引用

22nd ISPE-Inc international conference on Concurrent engineering

作者： Skarka, Wojciech Wylezol, Marek Januszka, Marcin Rzydzik, Sebastian Targosz, Miroslaw Silesian Tech Univ Inst Fundamentals Machinery Design Gliwice Poland

ISBN: (纸本)9781614995449;9781614995432

For technical field a lot of issues which are necessary to understand the subject is adjustable in the sphere of abstraction. For people, who do not have adequate knowledge, imagining even a simple element/assembly based on its orthogonal projections is not a significant problem. Such problems are well known and are the main goal of our research. The objectives of the project InterEdu was mainly to increase the quality of education. The ability to apply the techniques of passive and active 3D is assumed by default for the future engineer applying concurrent engineering methods. Aiding the teaching process with the use of a system which allows presentations of knowledge and data in a 3D virtual reality environment should result in a more efficient way to browse, evaluate, interpret and hence better understand the presented contents. The application of these techniques in a variety of courses not only makes it easy to explore the content of these courses but also allows students to become familiar with the use of sophisticated data visualization and to cooperate in various fields. The aim of the validation studies was to evaluate the operation and usefulness of the system in the context of its use in order to facilitate learning. The evaluation has been subjected to issues how the visualization of knowledge influences the effectiveness of the process of teaching. The students, taking part in the evaluation process, indicated the usefulness of this system in the teaching process. The InterEdu system improves the process of education and effectively aids the students who have difficulties in the content interpretation, especially at the earlier years of study.

关键词： computer-aided design concurrent engineering education interactive presentation virtual reality

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Search engine optimization proceb: A concurrent intelligent computing approach 22

Search engine optimization proceb: A concurrent intelligent ...

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22nd ISPE Inc. international conference on Concurrent engineering, CE 2015

作者： Sagot, Sylvain Fougères, Alain-Jérôme Ostrosi, Egon UTBM University of Technology of Belfort-Montbéliard Belfort90010 France IRTES-M3M ESTA School of Busineb and Engineering Belfort90010 France

ISBN: (纸本)9781614995432

The modification of customers' behaviors, the competition and the increase in the number of websites have forced companies to improve their visibility on the Internet. Search engines are widely used by customers and companies have to be present in the search engines results pages to be able to reach their clients. Different techniques have been developed to optimize the website's ranking on search engines, such as the SEO (Search Engine Optimization). The search engine optimization is the proceb of improving a website's position in the Internet search engine results. But the search engines protect their ranking models and results are long and difficult to obtain. Thus the non-Transparency of ranking models, the important number of interactions, and the uncertainty in terms of results make the SEO proceb a complex problem. In order to permit its adaptability and sustainability in a dynamic and uncertain environment, the SEO proceb needs the elaboration of holistic and concurrent engineering approaches. In this paper, we used the multi-Agent paradigm which is appropriate to solve concurrent and distributed problems. By decomposing the SEO proceb in sub-entities represented by communities of autonomous agents such as requirements, functions, constraints and solutions, we were able to analyze interactions and actions that take place into this proceb. A multi-Agent based simulation using data from pharmacy websites was developed to test our approach. © 2015 The authors and IOS Preb.

关键词： Multi agent systems

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Distributed learning game based spectrum sharing and resource selection for femtocells

Distributed learning game based spectrum sharing and resourc...

引用

international conference on Telecommunications (ICT)

作者： Ghassan Alnwaimi Seiamak Vahid Klaus Moessner Electrical and Computer Engineering Department King Abdulaziz University Institute for Communication Systems University of Surrey

This work investigates enabling technology for spectrum sharing in heterogeneous networks (HetNets) deployment, particularly, when a layer of femtocells (FCs) overlaid upon a mobile cellular network. We propose a fully distributed strategic learning based model that enables Femtocells to autonomously identify spectrum use pattern, and accordingly select available resources, such as to operate under restrictions of avoiding interference and satisfy a certain QoS requirements. The simulation results show that the proposed model can identify unused spectral resources of underlying macrocells network, and FCs can autonomously adjust their spectrum resources and converge to a solution concept that satisfy both networks conditions. We show that intra/inter-tier interference can be reduced significantly, thus resulting in higher cell throughputs. Such a distributed intelligent scheme can provide a practical solution to the main challenges in opportunistic spectrum use and interference management in HetNets.

关键词： Interference Throughput Games Macrocell networks Femtocells Quality of service

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An adaptive hybrid scheduling algorithm for LTE-Advanced

An adaptive hybrid scheduling algorithm for LTE-Advanced

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international conference on Telecommunications (ICT)

作者： Basel Barakat Kamran Arshad Department of Electrical Electronic and Computer Engineering University of Greenwich United Kingdom

The 3 rd Generation Partnership Project (3GPP) introduced Long Term Evolution (LTE) in release 8, and afterwards it was updated significantly in later releases (referred to as LTE-Advanced). LTE and LTE-Advanced (LTE-A) aim to achieve higher spectral efficiency, higher data rates, robustness and flexibility. intelligent channel-aware radio resource scheduling is one of the key features of LTE-A. A number of schedulers proposed in the literature rely on the feedback sent from the Users Equipment (UE) without considering the presence of feedback delay. In this paper, we analyse the effect of the uplink delay on the cell performance of existing schedulers, in terms of throughput and the users' fairness. We then propose an adaptive hybrid scheduler to overcome the effect of the uplink delay on the scheduler performance. The simulation results show that our proposed scheduling algorithm outperforms the existing schedulers in the presence of uplink feedback delay.

关键词： Throughput Delays Uplink Long Term Evolution Scheduling algorithms Bandwidth

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Active learning from Video Streams in a Multi-Camera Scenario 22

Active Learning from Video Streams in a Multi-Camera Scenari...

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22nd international conference on Pattern Recognition (ICPR)

作者： Khoshrou, Samaneh Cardoso, Jaime S. Teixeira, Luis F. Univ Porto INESC TEC P-4100 Oporto Portugal Univ Porto Fac Engn P-4100 Oporto Portugal Univ Porto Fac Engn Dept Engn Informat P-4100 Oporto Portugal

ISBN: (纸本)9781479952083

While video surveillance systems are spreading everywhere, extracting meaningful information from what they are recording is still prohibitively expensive. There is a major effort under way in order to make this process economical by including an intelligent software that eases the burden of the system. In this paper, we introduce an incremental learning framework to classify parallel data streams generated in a multi-camera surveillance scenario. The framework exploits active learning strategies in order to interact wisely with operators to address various problems that exist in such non-stationary environments, such as concept drift and concept evolution. If we look at the problem as mining parallel streams, the framework can address learning from uneven parallel streams applying a class-based ensemble, a problem that has not been addressed before. Favourable results indicate the success of the framework.

关键词： Security systems

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Semi-Supervised learning for RGB-D Object Recognition 22

Semi-Supervised Learning for RGB-D Object Recognition

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22nd international conference on Pattern Recognition (ICPR)

作者： Cheng, Yanhua Zhao, Xin Huang, Kaiqi Tan, Tieniu Chinese Acad Sci Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp Beijing 100864 Peoples R China

ISBN: (纸本)9781479952083

Conventional supervised object recognition methods have been investigated for many years. Despite their successes, there are still two suffering limitations: (1) various information of an object is represented by artificial features only derived from RGB images;(2) lots of manually labeled data is required by supervised learning. To address those limitations, we propose a new semi-supervised learning framework based on RGB and depth (RGB-D) images to improve object recognition. In particular, our framework has two modules: (1) RGB and depth images are represented by convolutional-recursive neural networks to construct high level features, respectively;(2) co-training is exploited to make full use of unlabeled RGB-D instances due to the existing two independent views. Experiments on the standard RGB-D object dataset demonstrate that our method can compete against with other state-of-the-art methods with only 20% labeled data.

关键词： Accuracy Cameras Feature extraction Object recognition Semisupervised learning Shape Training

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learning Convolutional NonLinear Features for K Nearest Neighbor Image Classification 22

Learning Convolutional NonLinear Features for K Nearest Neig...

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22nd international conference on Pattern Recognition (ICPR)

作者： Ren, Weiqiang Yu, Yinan Zhang, Junge Huang, Kaiqi CASIA NLPR Ctr Res Intelligent Percept & Comp Beijing 100190 Peoples R China Baidu Inc Beijing Peoples R China

ISBN: (纸本)9781479952083

learning low-dimensional feature representations is a crucial task in machine learning and computer vision. Recently the impressive breakthrough in general object recognition made by large scale convolutional networks shows that convolutional networks are able to extract discriminative hierarchical features in large scale object classification task. However, for vision tasks other than end-to-end classification, such as K Nearest Neighbor classification, the learned intermediate features are not necessary optimal for the specific problem. In this paper, we aim to exploit the power of deep convolutional networks and optimize the output feature layer with respect to the task of K Nearest Neighbor (kNN) classification. By directly optimizing the kNN classification error on training data, we in fact learn convolutional nonlinear features in a data-driven and task-driven way. Experimental results on standard image classificatioin benchmarks show that the proposed method is able to learn better feature representations than other general end-to-end classification methods on kNN classification task.

关键词： Algorithm design and analysis Computational modeling Convolutional codes data models Measurement Neural networks Training

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Activity recognition for a Smartphone based travel survey based on cross-user history data 22

Activity recognition for a Smartphone based travel survey ba...

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22nd international conference on Pattern Recognition (ICPR)

作者： Kim, Youngsung Pereira, Francisco C. Zhao, Fang Ghorpade, Ajinkya Zegras, P. Christopher Ben-Akiva, Moshe SMART Singapore 138602 Singapore MIT Cambridge MA 02139 USA

ISBN: (纸本)9781479952083

In transport modeling and prediction, trip purposes play an important role. The most particular case is activity-based modeling, whereby mobility choices (e.g. mode, path, and departure time) are made in order to carry out specific activities. A current challenge, however, lies on getting appropriate data that relates observed trips with their purpose. Recently, a Smartphone-based travel survey (the Future Mobility Survey, FMS) was conducted in Singapore that collected location data from 793 participants. Each FMS user was required to collect data for at least 14 days and validate at least 5 of them. This dataset presents diverse opportunities in terms of developing machine learning models for the future versions of FMS, where the validation process is intelligent and easy to use (e.g. having pre-filled activities associated to the user traces). This paper proposes a learning model that, given a stop location, identifies the most likely activity associated to it. Our data often contains errors or noise due to limited functionality of physical sensors in a dense area, and human mistakes in the validation process. To alleviate this effect, we generate heterogeneous features by different spatial quantization techniques and apply ensemble learning for a good generalization performance.

关键词： Smartphones

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Semi-Supervised learning of Geospatial Objects Through Multi-Modal data Integration 22

Semi-Supervised Learning of Geospatial Objects Through Multi...

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22nd international conference on Pattern Recognition (ICPR)

作者： Yang, Yi Newsam, Shawn Univ Calif Elect Engn & Comp Sci Merced CA 95343 USA

ISBN: (纸本)9781479952083

We investigate how overhead imagery can be integrated with non-image geographic data to learn appearance models for geographic objects with minimal user supervision. While multi-modal data integration has been successfully applied in other domains, such as multimedia analysis, significant opportunity remains for similar treatment of geographic data due to location being a simple yet powerful key for associating varying data modalities, and the growing availability of data annotated with location information either explicitly or implicitly. We present a specific instantiation of the framework in which overhead imagery is combined with gazetteers to compensate for a recognized deficiency: most gazetteers are incomplete in that the same latitude/longitude point serves as the bounding coordinates of the spatial extent of the indexed objects. We use a hierarchical object appearance model to estimate the spatial extents of these known object instances. The estimated extents can then be used to revise the gazetteers. A particularly novel contribution of our work is a semi-supervised learning regime which incorporates weakly labelled training data, in the form of incomplete gazetteer entries, to improve the learned models and thus the spatial extent estimation.

关键词： Computational modeling data models Equations Feature extraction Mathematical model Training data Visualization Training data data models data integration Computational modeling Gazetteers Feature extraction Mathematical Model Visualization spatial extent Semi-supervised learning data availability

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