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Correction to: Compositional and genetic alterations in Graves' disease gut microbiome reveal specific diagnostic biomarkers

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The ISME journal 2022年第4期16卷 1201页

作者： Qiyun Zhu Qiangchuan Hou Shi Huang Qianying Ou Dongxue Huo Yoshiki Vázquez-Baeza Chaoping Cen Victor Cantu Mehrbod Estaki Haibo Chang Pedro Belda-Ferre Ho-Cheol Kim Kaining Chen Rob Knight Jiachao Zhang Department of Pediatrics University of California San Diego La Jolla CA USA. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. School of Food Science and Engineering Hainan University Haikou China. Department of Endocrinology Hainan General Hospital Hainan Affiliated Hospital of Hainan Medical University Haikou China. Hubei Provincial Engineering and Technology Research Center for Food Ingredients Hubei University of Arts and Science Xiangyang Hubei province China. Key Laboratory of Food Nutrition and Functional Food of Hainan Province Haikou China. Department of Bioengineering University of California San Diego La Jolla CA USA. Scalable Knowledge Intelligence IBM Research-Almaden San Jose CA USA. School of Food Science and Engineering Hainan University Haikou China. kainch@***. Department of Endocrinology Hainan General Hospital Hainan Affiliated Hospital of Hainan Medical University Haikou China. kainch@***. Department of Pediatrics University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Center for Microbiome Innovation Jacobs School of Engineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Bioengineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Computer Science and Engineering University of California San Diego La Jolla CA USA. robknight@ucsd.edu. Department of Pediatrics University of California San Diego La Jolla CA USA. zhjch321123@***. School of Food Science and Engineering Hainan University Haikou China. zhjch321123@***. Key Laboratory of Food Nutrition and Functional Food of Hainan Province Haikou China. zhjch321123@***.

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Expert finding in community question answering: A review

arXiv

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arXiv 2018年

作者： Yuan, Sha Zhang, Yu Tang, Jie Cabotà, Juan Bautista Knowledge Engineering Lab Department of Computer Science and Technology Tsinghua University Institute of Medical Information Peking Union Medical College Chinese Academy of Medical Sciences Computer Science Department University of Valencia

The rapid development recently of Community Question Answering (CQA) satisfies users quest for professional and personal knowledge about anything. In CQA, one central issue is to find users with expertise and willingness to answer the given questions. Expert finding in CQA often exhibits very different challenges compared to traditional methods. Sparse data and new features violate fundamental assumptions of traditional recommendation systems. This paper focuses on reviewing and categorizing the current progress on expert finding in CQA. We classify all the existing solutions into four different categories: matrix factorization based models (MF-based models), gradient boosting tree based models (GBT-based models), deep learning based models (DL-based models) and ranking based models (R-based models). We find that MF-based models outperform other categories of models in the field of expert finding in CQA. Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boosting the performance. Further, we compare the performance of different models on different types of matching tasks, including text vs. text, graph vs. text, audio vs. text and video vs. text. The results can help the model selection of expert finding in practice. Finally, we explore some potential future issues in expert finding research in CQA. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep learning

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Accumulated gradient normalization 9

Accumulated gradient normalization

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9th Asian Conference on Machine Learning, ACML 2017

作者： Hermans, Joeri R. Spanakis, Gerasimos Möckel, Rico Department of Electrical Engineering and Computer Science Liège University Belgium Department of Data Science and Knowledge Engineering Maastricht University Netherlands

This work addresses the instability in asynchronous data parallel optimization. It does so by introducing a novel distributed optimizer which is able to efficiently optimize a centralized model under communication constraints. The optimizer achieves this by pushing a normalized sequence of first-order gradients to a parameter server. This implies that the magnitude of a worker delta is smaller compared to an accumulated gradient, and provides a better direction towards a minimum compared to first-order gradients, which in turn also forces possible implicit momentum fluctuations to be more aligned since we make the assumption that all workers contribute towards a single minima. As a result, our approach mitigates the parameter staleness problem more effectively since staleness in asynchrony induces (implicit) momentum, and achieves a better convergence rate compared to other optimizers such as asynchronous easgd and dynsgd, which we show empirically. © 2017 J.R. Hermans, G. Spanakis & R. Möckel.

关键词： Optimization

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A hybrid intrusion detection system: Integrating hybrid feature selection approach with heterogeneous ensemble of intelligent classifiers

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International Journal of Network Security 2018年第1期20卷 41-55页

作者： Amrita Ravulakollu, Kiran Kumar Department of Computer Science and Engineering Sharda University SET Plot No. 32-34 Knowledge Park III Greater Noida Uttar Pradesh201306 India

This paper proposes Hybrid Feature Selection Approach - Heterogeneous Ensemble of Intelligent Classifiers (HyFSA-HEIC) for intelligent lightweight network intrusion detection system (NIDS). The purpose is to classify for anomaly from the incoming traffic. This system hierarchically integrates HyFSA and HEIC. The HyFSA will obtain the optimal number of features and then HEIC is built using these optimal features. HyFSA helps to decrease the computation time of the system and make it lightweight to work in real time. The aim of HEIC is to obtain accurate and robust classifier and enhance overall performance of the system. The results demonstrate that proposed system outperforms other ensemble and single classifier methods used in this paper. It has true positive rate (99.9%), accuracy (99.91%), precision (99.9%), receiver operating characteristics (99.9%), low false positive rate (0.1%) and lower root mean square error rate (3.06%) with a minimum number of selected 6 features. It also reduces time to build and time to test the model by 50.79% and 55.30% respectively on reduced features set. The results evince that detection rate, accuracy and precision of the system is increased by incorporating feature selection approach with heterogeneous ensemble of intelligent classifiers and significantly reduce the computation time. © The Japan Society for Analytical Chemistry.

关键词： Feature Selection

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An Efficient Jaya Algorithm for Resource Allocation in the Cognitive-Radio-Networks-Aided Internet of Things

An Efficient Jaya Algorithm for Resource Allocation in the C...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Xiong Luo Zhijie He Long Wang Weiping Wang Huansheng Ning Jenq-Haur Wang Wenbing Zhao Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing (USTB) Beijing China Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering and Computer Science Cleveland State University Cleveland Ohio USA

More recently, there has been an ever-increasing demand for communication network bandwidth in Internet of Things (IoT), while requiring more and more powerful technologies in using scarce spectrum resources. Given the success of big data and artificial intelligence methods in a variety of industrial applications, it is expected that they can be also employed successfully to address the above issue. Cognitive radio networks (CRNs) identified as one of the potential solutions to improve the utilization of scarce radio spectrum resources, enable IoT to be achieved with high-performance. While in CRNs aided IoT systems, dynamic resource allocation is the main task. Then, orthogonal frequency division multiplexing (OFDM) as a multi-carrier parallel radio transmission technology, has been identified as one of the main approaches well-matched for CRNs aided IoT systems. In this paper, motivated by swarm intelligence paradigm, a solution method is proposed by applying an enhanced Jaya algorithm, named S-Jaya, to address the power allocation problem in cognitive OFDM radio networks for IoT. Due to the algorithm-specific parameter-free feature of the proposed Jaya algorithm with fast convergence speed, a satisfactory computational performance would be achieved in handling this problem. The simulation results show that, for the optimization problem with some constraints, the efficiency of spectrum utilization could be further improved through the use of S-Jaya algorithm, while maximizing the total transmission rate with faster convergence speed, compared with some popular algorithms.

关键词： OFDM Resource management Optimization Interference Internet of Things Convergence Bandwidth

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Ontology Driven Semantic Profiling and Retrieval in Medical Information Systems

SSRN

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SSRN 2018年

作者： Bhatt, Mehul Rahayu, Wenny Soni, Sury Prakash Wouters, Carlo Cognitive Systems Lab Universität Bremen Bremen28359 Germany Data Engineering and Knowledge Management Group Department of Computer Science and Computer Engineering La Trobe University Australia

We propose the application of a novel sub-ontology extractionmethodology for achieving interoperability and improving the semantic validity of information retrieval in the medical information systems (MIS) domain. The system offers advanced profiling of a user’s field of specialization by exploiting the concept of sub-ontology extraction, i.e., each sub-ontology may subsequently represent a particular user profile. Semantic profiling of a user’s field of specialization or interest is necessary functionality in any medical domain information retrieval system;this is because the (structural and semantic) extent of information sources is massive and individual users are only likely to be interested in specific parts of the overall knowledge documents on the basis of their area of specialization. The prototypical system, OntoMOVE, has been specifically designed for application in themedical information systems domain. OntoMOVE utilizes semantic web standards like RDF(S) and OWL in addition to medical domain standards and vocabularies encompassed by the UMLS knowledge sources. © 2018, The Authors. All rights reserved.

关键词： Medical information systems

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Remotely accessible, automatic file downloading system with interactive smartphone application using embedded system

Remotely accessible, automatic file downloading system with ...

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2017 International Conference on Electronics, Communication and Aerospace Technology, ICECA 2017

作者： Faheem, Mohammed Sajid, Mohammed Yusra, K Nasif, Muhammed Ali, Akbar Sanal Kumar, T.S. Department of Computer Science Engineering Eranad Knowledge City Technical Campus ManjeriKerala India

ISBN: (纸本)9781509056873;9781509056866

Internet plays a significant role that it has helped the mankind to be advanced. One of the remarkable applications of Internet is the feature that enables to download any form of data. In order to accomplish it, devices are required that has specific functions. When a PC works for downloading purpose, it uses all its resources, though they are not wholly required and leads to large power consumption. An embedded system is being proposed to be designed so as to perform uninterrupted or continuous download with very low energy consumption. The system is fabricated using an embedded circuit which isa small single board computer stamped with chips and I/O connectors. The employing of cloud Server as an intermediate online database has made the system more efficient becausecloud Server system hasbeen emerged as a novel trend in the research fields. Cloud helps in processing the exchange of data between android application and the embedded system. © 2017 IEEE.

关键词： Embedded systems

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Residual network with delayed max pooling for very large scale plant identification 18

Residual network with delayed max pooling for very large sca...

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18th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2017

作者： Hang, Siang Thye Aono, Masaki Knowledge Data Engineering and Information Retrieval Laboratory Department of Computer Science and Engineering Toyohashi University of Technology Japan

In our approach, we applied a few modifications to the 50-layered Residual Network. Our preliminary experiments with the Plant-CLEF 2016 dataset showed that the modifications improved classification performance. We have trained three models based on the modified Residual Network configuration with different combinations of trusted and noisy PlantCLEF 2017 datasets. Using confidence scores extracted from the three models, we have submitted four runs and our methods showed competitive classification performance.

关键词： Deep learning

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International Conference on Advances in Computing, Communication Control and Networking (ICACCCN)

作者： Aditi Gupta Neetika Raina Bharti Jha Vinay Kumar Jain Department of Computer Science & Engineering Manav Rachna University Faridabad Haryana Accendere Knowledge Management Pvt Ltd. New Delhi

ISBN: (纸本)9781538641200

With an increasing usage of social media to exchange, share and store information, cybercriminals also get attracted to it, to take advantage of the network for their illegal and unethical benefits. Fake online accounts pop up every day. Spammers are the users behind the screen who share unsolicited and irrelevant texts to a huge number of users with an intent of advertising some product or to make people to click on unsecured links and infecting user's system usually to make money (click bait). They often use Trending topics on social media as a medium to spam. Sometimes, spam and fake trending is created by Spammers and many a times spammers use Trending topics to lure victims into clicking them. Much research has been done and is going on to detect spammers in OSNs. This paper reviews the existing techniques to detect spammers in social media. Our Current study and future work provides an overview of the traditional classifiers, Naïve Bayes, Support Vector and how they are used to detect spam and classify a dataset taken from social media into trending and non-trending topics based spam.

关键词： Twitter Classification algorithms Support vector machine classification computer science Facebook

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Cloud based automated irrigation and plant leaf disease detection system using an android application

Cloud based automated irrigation and plant leaf disease dete...

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2017 International Conference on Electronics, Communication and Aerospace Technology, ICECA 2017

作者： Ranjith Anas, Saheer Badhusha, Ibrahim Ot, Zaheema Faseela, K. Shelly, Minnuja Department of Computer Science Engineering Eranad Knowledge City Technical Campus ManjeriKerala India

ISBN: (纸本)9781509056873;9781509056866

Due to drastic climatic changes and scarcity of water, the need for proper and sustainable irrigation methods is of high demand. The water demand for plants varies from place to place with the changes in soil content, texture, climatic factors and so on. The plants need to be irrigated according to their water requirements at that climatic conditions. As like as the water requirements, plant diseases are also a factor that keeps the plants not growing properly. Also, a variety of plant leaf diseases have also been detected nowadays. In this paper, a novel smart irrigation system has been proposed which can control the irrigation automatically using an android mobile application. Apart from this, the photos of plant leaves are captured and are sent to the cloud server, which is further processed and compared with the diseased plant leaf images in the cloud database. Based on the comparison a list of plant diseases suspected are given to the user via the android mobile application. © 2017 IEEE.

关键词： Embedded systems

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