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9th IEEE International Conference on Data Mining

作者： Cai, Yuanzhe Cong, Gao Jia, Xu Liu, Hongyan He, Jun Lu, Jiaheng Du, Xiaoyong Key Labs of Data Engineering and Knowledge Engineering Ministry of Education China Department of Computer Science Renmin University of China China Department of Computer Science Aalborg University Denmark Department of Management Science and Engineering Tsinghua University China

ISBN: (纸本)9781424452422

Similarity calculation has many applications, such as information retrieval, and collaborative filtering, among many others. It has been shown that link-based similarity measure, such as SimRank, is very effective in characterizing the object similarities in networks, such as the Web, by exploiting the object-to-object relationship. Unfortunately, it is prohibitively expensive to compute the link-based similarity in a relatively large graph. In this paper, based on the observation that link-based similarity scores of real world graphs follow the power-law distribution, we propose a new approximate algorithm, namely Power-SimRank, with guaranteed error bound to efficiently compute link-based similarity measure. We also prove the convergence of the proposed algorithm. Extensive experiments conducted on real world datasets and synthetic datasets show that the proposed algorithm outperforms SimRank by four-five times in terms of efficiency while the error generated by the approximation is small.

关键词： Similarity Calculation SimRank Graph Mining

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Bi-directional search for robust routes in time-dependent bi-criteria road networks 15

Bi-directional search for robust routes in time-dependent bi...

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15th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2015

作者： Mihalák, Matú Montanari, Sandro Department of Knowledge Engineering Maastricht University Netherlands Department of Computer Science ETH Zurich Switzerland

ISBN: (纸本)9783939897996

Based on time-dependent travel times for N past days, we consider the computation of robust routes according to the min-max relative regret criterion. For this method we seek a path minimizing its maximum weight in any one of the N days, normalized by the weight of an optimum for the respective day. In order to speed-up this computationally demanding approach, we observe that its output belongs to the Pareto front of the network with time-dependent multi-criteria edge weights. We adapt a well-known algorithm for computing Pareto fronts in time-dependent graphs and apply the bi-directional search technique to it. We also show how to parametrize this algorithm by a value K to compute a K-approximate Pareto front. An experimental evaluation for the cases N = 2 and N = 3 indicates a considerable speed-up of the bi-directional search over the uni-directional.

关键词： Travel time

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Architecture of Bluetooth Security for Pairing Key and Better Authentication 5

Architecture of Bluetooth Security for Pairing Key and Bette...

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5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Mondal, Asif Ikbal Mandal, Bijoy Kumar Dumkal Institute of Engineering Technology Department of Computer Science and Engineering Murshidabad742406 India NSHM Knowledge Campus Department of Computer Science and Engineering Durgapur713212 India

ISBN: (纸本)9781665403412

In this paper, we have deal with the authentication and identification of the device for higher security using key pairing and low computation. Bluetooth when start communication with other device it needs pairing, and then a number of other processes, Profile selection which defines the types of communication. The connection can be compromised and without pairing codes connection can be established. Once the connection is established passing command to the victim's device makes the device to work in favor of the attacker without being noticed by the owner of the device. One such tool is bluesnarfer which is used to pair two devices without being acknowledged by the owner or the victim. This paper is proposed for finding a solution to the same using asymmetric key cryptography and encrypted protocol values to gain access of the different function of the Bluetooth device. The solution is deployed in a number of phases. Public and private key based pairing is used which make the authentication more effective. This is the first phase. In the next second phase there will be we verification protocol in which referential connection is required for making a new connection. In the third phase information regarding profile choice is passed through a tunnel profile, which passed on to the actual profile required. This system contains a threshold function which defines that if at any stage a breach of the system is identified then the system will promote the attacker with improper information to miss lead the attacker so that either the attacker is kept stuck in a miss leading loop or the connection is closed. © 2021 IEEE.

关键词： Bluetooth

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CTSC: Core-tag oriented spectral clustering algorithm on Web2.0 tags

CTSC: Core-tag oriented spectral clustering algorithm on Web...

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6th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2009

作者： Jiang, Yexi Tang, Changjie Xu, Kaikuo Chen, Yu Tang, Liang Database and Knowledge Engineering Lab. Department of Computer Science Sichuan University China

ISBN: (纸本)9780769537351

With the rapid development of the Web2.0 communities, many researchers have been attracted by the concept of folksonomy from the field of data mining and information retrieval. Finding out semantic correlation of tags is avid requirement for web2.0 application. However, no proper algorithm can tackle this task very well. This paper proposes a core-tag oriented clustering method to handle the task. The main contributions include: (1) Proposing the concept of core-tag oriented space;(2) Proposing a method called Core-Tag oriented Spectral Clustering (CTSC) to cluster tags in the new space;(3) Designing experiments to evaluate the algorithm, and the results show that CTSC algorithm performs well on clustering tags. © 2009 IEEE.

关键词： Data mining

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Building linked open university data: tsinghua university open data as a showcase

Building linked open university data: tsinghua university op...

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Joint International Semantic Technology Conference, JIST 2011

作者： Ma, Yuanchao Xu, Bin Bai, Yin Li, Zonghui Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783642299223

Linked Open University Data applies semantic web and linked data technology to university data scenario, aiming at building interlinked semantic data around university information, providing possibility for unified inner- and inter- school information query and comparison. This paper proposes a general process of building linked open university data, with procedures covering choosing datasets and vocabularies, collecting and processing data, building RDF and interlink, etc. Tsinghua University Open Data is used to demonstrate the process. Tsinghua University consist of 5 well-formed, interconnected datasets, with a number of interesting applications has been built on top of them. Finally, remarkable points about data collecting and processing is discussed. © 2012 Springer-Verlag.

关键词： Linked data

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Sentilyzer: Aspect-oriented sentiment analysis of product reviews

Sentilyzer: Aspect-oriented sentiment analysis of product re...

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2018 International Conference on Computational science and Computational Intelligence, CSCI 2018

作者： Wladislav, Schulz Johannes, Zenkert Christian, Weber Andre, Klahold Madjid, Fathi Department of Electrical Engineering and Computer Science Institute of Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany

ISBN: (纸本)9781728113609

Aspect-oriented sentiment analysis is used in particular on textual product reviews to identify positive or negative product characteristics. This information is beneficial not only for customers related to purchasing decisions, but also used by manufacturers to improve their products. In this short paper, we introduce Sentilyzer, a system which performs state-of-the-art aspect-oriented sentiment analysis based on automatically generated category-specific sentiment and aspect dictionaries from Amazon product reviews. © 2018 IEEE.

关键词： Sentiment analysis

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8th Asia-Pacific Web Conference, APWeb 2006: Frontiers of WWW Research and Development

作者： Liang, Bangyong Tang, Jie Li, Juanzi Wang, Kehong Knowledge Engineering Group Department of Computer Science Tsinghua University 100084 Beijing China

ISBN: (纸本)3540311424

This paper addresses the issue of ontology caching on semantic web. The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. Ontology serves as the metadata for defining the information on semantic web. Ontology based semantic information retrieval (semantic retrieval) is becoming more and more important. Many research and industrial works have been made so far on semantic retrieval. Ontology based retrieval improves the performance of search engine and web mining. In semantic retrieval, a great number of accesses to ontologies usually lead the ontology servers to be very low efficient. To address this problem, it is indeed necessary to cache concepts and instances when ontology server is running. Existing caching methods from database community can be used in the ontology cache. However, they are not sufficient for dealing with the problem. In the task of caching in database, usually the most frequently accessed data are cached and the recently less frequently accessed data in the cache are removed from it. Different from that, in ontology base, data are organized as objects and relations between objects. User may request one object, and then request another object according to a relation of that object. He may also possibly request a similar object that has not any relations to the object. Ontology caching should consider more factors and is more difficult. In this paper, ontology caching is formalized as a problem of classification. In this way, ontology caching becomes independent from any specific semantic web application. An approach is proposed by using machine learning methods. When an object (e.g. concept or instance) is requested, we view its similar objects as candidates. A classification model is then used to predict whether each of these candidates should be cached or not. Features in classification models are defined. Experimental results indicat

关键词： Data structures

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A collaborative filtering tag recommendation system based on graph

A collaborative filtering tag recommendation system based on...

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International Workshop on Discovery Challenge, DC 2009 at the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in Databases, ECML PKDD 2009

作者： Zhang, Yuan Zhang, Ning Tang, Jie Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University Beijing China

With the rapid development of web2.0 technologies, tagging become much more important today to organize information and help users search the information they need with social bookmarking tools. In order to finish the second task of ECML PKDD challenge 2009, we propose a graph-based collaborative filtering tag recommendation system. We also refer to an algorithm called FolkRank, which is an adaptation of the famous Page Rank. We evaluate and compare these two approaches and show that a combination of these two methods will perform better results for our task.

关键词： Collaborative filtering

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A tag recommendation system based on contents

A tag recommendation system based on contents

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International Workshop on Discovery Challenge, DC 2009 at the European Conference on Machine Learning and Principles and Practice of knowledge Discovery in Databases, ECML PKDD 2009

作者： Zhang, Ning Zhang, Yuan Tang, Jie Knowledge Engineering Group Department of Computer Science and Technology Tsinghua University Beijing China

Social bookmarking tools become more and more popular nowadays and tagging is used to organize information and allow users to recall or search the resources. Users need to type the tags whenever they post a resource, so that a good tag recommendation system can ease the process of finding some useful and relevant keywords for users. Researchers have made lots of relevant work for recommendation system, but those traditional collaborative systems do not fit to our tag recommendation. In this paper, we present two different methods: a simple language model and an adaption of topic model. We evaluate and compare these two approaches and show that a combination of these two methods will perform better results for the task one of PKDD Challenge 2009.

关键词： Recommender systems

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Intelligent waste classification approach based on improved multi-layered convolutional neural network

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Multimedia Tools and Applications 2024年第36期83卷 84095-84120页

作者： Chhabra, Megha Sharan, Bhagwati Elbarachi, May Kumar, Manoj Department of Computer Science & amp Engineering School of Engineering & ampTechnology Sharda University UP Gr. Noida India School of Engineering and Sciences Department of Computer Science and Engineering SRM University-AP Andhra Pradesh Amaravati India Head of School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates MEU Research Unit Middle East University Amman11831 Jordan

This study aims to improve the performance of organic to recyclable waste through deep learning techniques. Negative impacts on environmental and Social development have been observed relating to the poor waste segregation schemes. Separating organic waste from recyclable waste can lead to a faster and more effective recycling process. Manual waste classification is a time-consuming, costly, and less accurate recycling process. Automated segregation in the proposed work uses Improved Deep Convolutional Neural Network (DCNN). The dataset of 2 class category with 25077 images is divided into 70% training and 30% testing images. The performance metrics used are classification Accuracy, Missed Detection Rate (MDR), and False Detection Rate (FDR). The results of Improved DCNN are compared with VGG16, VGG19, MobileNetV2, DenseNet121, and EfficientNetB0 after transfer learning. Experimental results show that the image classification accuracy of the proposed model reaches 93.28%. © The Author(s) 2024.

关键词： Convolutional neural networks

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