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Achieving a balanced optimisation between network lifetime and data delivery latency in multihop wireless sensor networks

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International Journal of Autonomous and Adaptive Communications Systems 2019年第3期12卷 244-263页

作者： Babu, Ravi Shenoy, Udaya Kumar K. Patil, Kiran Kumari REVA University Rukmini Knowledge Park Yelahanka Kattigenahalli Bengaluru Karnataka560064 India Department of Computer Science and Engineering NMAM Institute of Technology NITTE SH1 Karkala Karnataka574110 India School of Computing and IT REVA University Rukmini Knowledge Park Yelahanka Kattigenahalli Bengaluru Karnataka560064 India

Data gathering using mobile sinks is considered one of the efficient and cost-effective strategies to mitigate the hotspot problem in wireless sensor networks (WSNs). Although sink mobility is beneficial in many ways, these benefits are not without its new challenges. A major concern is achieving a good balance between sensors’ energy consumption and message delivery latency. The unique characteristics and requirements of wireless sensor networks make this task a significant challenge. Another major challenge is coping with the changing topology caused by the sink mobility. The sensor nodes are required to maintain fresh routes toward the latest sink location for efficient data delivery. This paper proposes data gathering schemes to address these two major issues in WSNs by using a virtual overlay structure built over the existing physical network. Simulation results reveal a reduced route reconstruction cost and a good trade-off between network life-time and data delivery latency. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Energy utilization

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Semi-supervised Multi-kernel Extreme Learning Machine

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Procedia computer science 2018年 129卷 305-311页

作者： Adnan OM Abuassba Zhang Dezheng Zahid Mahmood Department of Computer Science and Communication Engineering University of Science and Technology and Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083 China

Extreme learning machine (ELM) is a single hidden layer feed forward neural network (SLFN). It expanded to semi-supervised ELM (SSELM) to deal with unlabeled data problem. In such a problem, labeled data is either rare or not cheap. Although SSELM has a good generalization performance, it might be influenced by heterogeneous data from different sources. It is discernible that unbalanced data issue inflicts obstacles in real-world applications including medical diagnostics and credit card fraud detection. To deal with this issue in this paper, we introduce a multi-kernel semi-supervised ELM (MKSSELM). It is more flexible to deal with discrete data from various sources. It matches diverse information from disparate sources and it shows distinction among the data. Instead of using one kernel, we optimize both ELM structural parameters and kernel combination weights. The optimization process accomplished by commanding an L1-norm as a regulation term. Meanwhile, a non-negative constraint on the kernel combination weights is used. The validity of MKSSELM algorithm is confirmed through classification results on real-world benchmark datasets. The proposed algorithm achieved better or comparable results with respect to previous approaches.

关键词： Classification Kernel Learning Semi-supervised Learning Multi-Kernel Learning Extreme Learning Machine

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Consensus with Voting Theory in Blockchain Environments

Consensus with Voting Theory in Blockchain Environments

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IEEE International Conference on Big knowledge (ICBK)

作者： Lei Li Yongkang Jiang Guanfeng Liu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Computing Department Macquarie University Sydney Australia

A blockchain can be taken as a decentralized and distributed public database. In order to achieve data consistency of the system nodes, the execution of a consensus algorithm is necessary and required in the case of decentralized environments. Simply speaking, the consensus is that every node agrees on some record in the blockchain. There are many kinds of consensus algorithms in blockchain environments, and each consensus algorithm has its own proper application scenario. Here we firstly analysis and compare various popular consensus algorithms in blockchain environments, and then as voting theory has systematically studied the decision-making in a group, the traditional methods of voting theory is summarized and listed, including (Position) scoring rules, Copeland, Maximin, Ranked pairs, Voting trees, Bucklin, Plurality with runoff, Single transferable vote, Baldwin rule, and Nanson rule. Finally, we introduce the voting methods from voting theory to consensus algorithms in the blockchain to improve its performance.

关键词： Blockchain Consensus algorithm Bitcoin Voting knowledge engineering computer science Decision making

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Virtualization-A New Dimension of Big Data 8

Virtualization-A New Dimension of Big Data

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8th International Conference Confluence on Cloud Computing, Data science and engineering, Confluence 2018

作者： Jain, Ugain Kumar, Sonu Dubey, Shubham Sharma, Ochin Kumar Jain, Vinay Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad Haryana India Accendere Knowledge Management Services Pvt. Ltd. India

ISBN: (纸本)9781538617182

Big data is a term the data or figures that are so complex that normal data collecting applications are unable to manage them. And with the increasing amount of data, challenges of accessing the data also arise. The world's capacity to store information is increasing very fast so we required to store and manage the data properly according to survey the data available to us will doubled in every 40 months since the 1980s to 2012, every day we produce 2.5 Exabyte's (2.5 × 1018) of data. The main challenge faced by big data is the storage issue. Big data require large storing capacity, which could not fulfill using the traditional storage based system. This challenge can be met by using the cloud system for warehousing. © 2018 IEEE.

关键词： Virtualization

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DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data

arXiv

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arXiv 2020年

作者： Chatterjee, Soumick Prabhu, Kartik Pattadkal, Mahantesh Bortsova, Gerda Sarasaen, Chompunuch Dubost, Florian Mattern, Hendrik de Bruijne, Marleen Speck, Oliver Nürnberger, Andreas Faculty of Computer Science Otto von Guericke University Magdeburg Magdeburg39106 Germany Data and Knowledge Engineering Group Otto von Guericke University Magdeburg39106 Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Magdeburg39106 Germany Biomedical Imaging Group Rotterdam Department of Radiology & Nuclear Medicine Erasmus MC Rotterdam3015 GD Netherlands Institute for Medical Engineering Otto von Guericke University Magdeburg Magdeburg39106 Germany Department of Computer Science University of Copenhagen CopenhagenDK-2100 Denmark German Center for Neurodegenerative Disease Magdeburg39120 Germany Center for Behavioral Brain Sciences Magdeburg39106 Germany

MSC Codes 68T07 (Primary) 68T45 (Secondary)Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer's disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi's vessel enhancement with subsequent thresholding, are capable of segmenting medium to large vessels but often fail to segment small vessels. The sensitivity of these methods to small vessels can be increased by extensive parameter tuning or by manual corrections, albeit making them time-consuming, laborious, and not feasible for larger datasets. This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a small imperfect semi-automatically segmented dataset of only 11 subjects;using six for training, two for validation, and three for testing. The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance. The proposed technique was evaluated quantitatively and qualitatively against the test set and achieved a Dice score of 80.44 ± 0.83. Furthermore, the result of the proposed method was compared against a selected manually segmented region (62.07 resultant Dice) and has shown a considerable improvement (18.98%) with deformation-aware learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Deformation

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Foundations of digital archæoludology

arXiv

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arXiv 2019年

作者： Browne, Cameron Soemers, Dennis J.N.J. Piette, Éric Stephenson, Matthew Conrad, Michael Crist, Walter Depaulis, Thierry Duggan, Eddie Horn, Fred Kelk, Steven Lucas, Simon M. Neto, João Pedro Parlett, David Saffidine, Abdallah Schädler, Ulrich Silva, Jorge Nuno de Voogt, Alex Winands, Mark H.M. Department of Data Science and Knowledge Engineering Maastricht University Netherlands Kunsthistorisches Institut Universität Zürich Switzerland Department of Anthropology American Museum of Natural History United States Department of Science Technology and Engineering University of Suffolk United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom University of Lisbon Portugal School of Computer Science and Engineering University of New South Wales Australia Swiss Museum of Games Switzerland Economics & Business Department Drew University United States

Digital Archæoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines – computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. – to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project. Copyright © 2019, The Authors. All rights reserved.

关键词： History

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Poster: Understanding customers' interests in the wild

Poster: Understanding customers' interests in the wild

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2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable computers, ISWC 2018

作者： Kang, Soowon Kim, Auk Shin, Ikhee Lee, Uichin Lee, Jemin Graduate School of Knowledge Service Engineering KAIST Daejeon Korea Republic of Department of Computer Science and Engineering Chungnam National University Daejeon Korea Republic of Industrial Engineering and Management Research Institute KAIST Daejeon Korea Republic of

ISBN: (纸本)9781450359665

Today, retailers spend considerable efforts to provide a personalized shopping experience to their customers. As data-driven marketing helps to meet customer requirements, it is important to understand individual needs. However, offline stores-unlike their online counterparts-have great difficulty knowing their customers' needs due to a lack of proper context information. In this paper, we proposed a framework for estimating customer interests by using various sensor devices. The participants in our pilot study expected that recommendation services that adopt their interests would help to reduce their shopping time. As a result, shop assistants will have a stronger ability to understand, analyze, and even predict customer interests in the near future. © Copyright held by the owner/author(s).

关键词： Sales

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Heterogeneous-length text topic modeling for reader-Aware multi-document summarization

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ACM Transactions on knowledge Discovery from Data 2019年第4期13卷 42-42页

作者： Qiang, Jipeng Chen, Ping Ding, Wei Wang, Tong Xie, Fei Wu, Xindong Department of Computer Science Yangzhou University China Department of Computer Science University of Massachusetts Boston United States Department of Computer Science and Technology Hefei Normal University China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100084 China

More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as reflected in reader comments. In this article, we focus on how to generate a summary from multi-document documents by considering reader comments, named as reader-Aware multi-document summarization (RA-MDS).We present an innovative topic-based method for RA-MDA, which exploits latent topics to obtain the most salient and lessen redundancy summary from multiple documents. Since finding latent topics for RA-MDS is a crucial step, we also present a Heterogeneous-length Text Topic Modeling (HTTM) to extract topics from the corpus that includes both news reports and user comments, denoted as heterogeneous-length texts. In this case, the latent topics extract by HTTM cover not only important aspects of the event, but also aspects that attract reader interests. Comparisons on summary benchmark datasets also confirm that the proposed RA-MDS method is effective in improving the quality of extracted summaries. In addition, experimental results demonstrate that the proposed topic modeling method outperforms existing topic modeling algorithms. © 2019 Association for Computing Machinery. All rights reserved.

关键词： heterogeneous-length text LDA multi-document summarization Topic modeling

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Adaptive Structure-Constrained Robust Latent Low-Rank Coding for Image Recovery

Adaptive Structure-Constrained Robust Latent Low-Rank Coding...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhao Zhang Lei Wang Sheng Li Yang Wang Zheng Zhang Zhengjun Zha Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Georgia Athens GA Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2, 1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery.

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Development of Precision Agriculture Models for Medium and Small-Scale Agriculture in Indonesia

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IOP Conference Series: Materials science and engineering 2020年第1期879卷

作者： H Maulana H Kanai School of Knowledge Science Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa Japan. Department of Informatics Engineering and Computer Science Universitas Komputer Indonesia Indonesia.

The purpose of this research is to build a model of precision agriculture to increase the productivity of medium and small-scale agriculture in Indonesia. This research used a descriptive method by conducting a literature study on the concept of precision agriculture and analysis of data related to agriculture in Indonesia. Extreme weather changes in Indonesia have an impact on the decline in agricultural production. While an understanding of environmental conditions is the primary key to agriculture, this research explained what data is needed in precision agriculture, how to obtain and process data, also explained how to visualize information through geographic information systems. This research also conducted an assessment of the built model. The assessment process is implemented by analysing the suitability of the model based on farmers' needs. The results of this study concluded that the model can be developed in the implementation phase. This research is expected to provide a clear explanation of the system requirements and also can reduce risk at the system development and implementation stage.

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