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Assessment of NER solutions against the first and second CALBC Silver Standard Corpus

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Assessment of NER solutions against the first and second CAL...

4th International Symposium on Semantic Mining in Biomedicine, SMBM 2010

作者： Rebholz-Schuhmann, Dietrich Yepes, Antonio Jimeno Li, Chen Kafkas, Senay Lewin, Ian Kang, Ning Corbett, Peter Milward, David Buyko, Ekaterina Beisswanger, Elena Hornbostel, Kerstin Kouznetsov, Alexandre Witte, René Laurila, Jonas B. Baker, Christopher J.O. Kuo, Cheng-Ju Clematide, Simone Rinaldi, Fabio Farkas, Richárd Móra, György Hara, Kazuo Furlong, Laura Rautschka, Michael Neves, Mariana Lara Pascual-Montano, Alberto Wei, Qi Collier, Nigel Chowdhury, Md. Faisal Mahbub Lavelli, Alberto Berlanga, Rafael Morante, Roser Van Asch, Vincent Daelemans, Walter Marina, José Luís Van Mulligen, Erik Kors, Jan Hahn, Udo EMBL Outstation European Bioinformatics Institute Hinxton Cambridge CB10 1SD United Kingdom Dept. of Medical Informatics Erasmus University Medical Center Rotterdam NL Netherlands Linguamatics Ltd. St. John's Innovation Centre Cambridge United Kingdom Lab. Friedrich-Schiller-Universität Jena Germany Dept. of Computer Science and Applied Statistics University of New Brunswick Canada Dept. of Computer Science and Software Engineering Concordia University Montreal Canada Institute of Information Science Academia Sinica Taipei 115 Taiwan University of Zürich Switzerland Research Group on Artificial Intelligence Hungarian Academy of Sciences Hungary Institute of Science and Technology Nara Japan IMIM DCEX Universitat Pompeu Fabra Barcelona Spain Madrid Spain National Institute of Informatics Tokyo Japan Fondazione Bruno Kessler Trento Italy Universitat Jaume I Spain CLiPS University of Antwerp Belgium Complutense University of Madrid Spain

Background: Text mining challenges have been organised to measure the performance of automatic text mining solutions against a manually annotated gold standard corpus (GSC). The preparation of the GSC is timeconsuming and costly and the final corpus consists at the most of a few thousand documents annotated with a limited set of semantic groups. To overcome these shortcomings, the CALBC project partners (PPs) have produced a large-scale annotated biomedical corpus with four different semantic groups through the harmonisation of annotations from automatic text mining solutions, the first version of the Silver Standard Corpus (SSC-I). The four semantic groups were chemical entities and drugs (CHED), genes and proteins (PRGE), diseases and disorders (DISO) and species (SPE). This corpus has been used for the First CALBC Challenge asking the participants to annotate the corpus with their annotation solutions.

关键词： Semantics

CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

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arXiv 2022年

作者： Lv, Jinkai Hu, Yuyong Fu, Quanshui Zhang, Zhiwang Hu, Yuqiang Lv, Lin Yang, Guoqing Li, Jinpeng Zhao, Yi Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China Suining Central Hospital Suining China Hwa Mei Hospital University of Chinese Academy of Sciences Ningbo China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Mathematics and Computer Sciences NanChang University Nanchang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multi-scale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our in-house dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://***/ProgrammerHyy/CM-MLP. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantic Segmentation

CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation ...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jinkai Lv Yuyong Hu Quanshui Fu Zhiwang Zhang Yuqiang Hu Lin Lv Guoqing Yang Jinpeng Li Yi Zhao Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Suining Central Hospital Suining China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Mathematics and Computer Sciences NanChang University Nanchang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Hwa Mei Hospital University of Chinese Academy of Sciences Ningbo China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multiscale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our inhouse dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://***/ProgrammerHyy/CM-MLP.

关键词： Convolutional codes Degradation Image segmentation Fuses Convolution Image edge detection Source coding

Encoding classical information into quantum resources

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arXiv 2019年

作者： Korzekwa, Kamil Puchala, Zbigniew Tomamichel, Marco Zyczkowski, Karol International Centre for Theory of Quantum Technologies University of Gdansk Gdańsk80-308 Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice44-100 Poland Centre for Quantum Technologies National University of Singapore Singapore117543 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Centre for Quantum Software and Information School of Software University of Technology Sydney SydneyNSW2007 Australia Center for Theoretical Physics Polish Academy of Sciences Warszawa02-668 Poland

We introduce and analyse the problem of encoding classical information into different resources of a quantum state. More precisely, we consider a general class of communication scenarios characterised by encoding operations that commute with a unique resource destroying map and leave free states invariant. Our motivating example is given by encoding information into coherences of a quantum system with respect to a fixed basis (with unitaries diagonal in that basis as encodings and the decoherence channel as a resource destroying map), but the generality of the framework allows us to explore applications ranging from super-dense coding to thermodynamics. For any state, we find that the number of messages that can be encoded into it using such operations in a one-shot scenario is upper bounded in terms of the information spectrum relative entropy between the given state and its version with erased resources. Furthermore, if the resource destroying map is the twirling channel over some unitary group, we find matching one-shot lower bounds as well. In the asymptotic setting where we encode into many copies of the resource state, our bounds yield an operational interpretation of resource monotones such as the relative entropy of coherence and its corresponding relative entropy variance. Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum optics

Machine Learning Approaches for Efficient Traffic Flow in Smart Cities

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Machine Learning Approaches for Efficient Traffic Flow in Sm...

Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Aniket Kulkarni P. Anitha John Yesudas Valluri M V Sunena Rose U Hemavathi Omar M. Hussein Software QA Manager Drata Inc San Diego CA USA Department of Computer Applications Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu India Faculty of Management Studies CMS Business School Jain (Deemed-to-be University) Bengaluru India Department of Computer Science and Engineering Jain (Deemed-to-be University) Bengaluru India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Medical Technical College Medical Instrumentation Engineering Al-Farahidi University Baghdad Iraq

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic stream prediction; this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML and DL computations suggest that their application to intelligent traffic light management is feasible.

关键词： Measurement Power engineering Machine learning algorithms Statistical analysis Smart cities Transportation Machine learning Prediction algorithms Timing Junctions

Receiver-based cross-layer forwarding protocol for mobile sensor networks

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Jisuanji Yanjiu yu Fazhan/computer Research and Development 2009年第1期46卷 120-128页

作者： Li, Lian Jiang, Wentao Sun, Limin Fan, Xiaozhong School of Computer Science and Technology Beijing Institute of Technology Beijing 100084 China Institute of Software Chinese Academy of Sciences Beijing 100190 China Department of Control Engineering Naval Aeronautical and Astronautical University Yantai 264001 China

As nodes can move randomly in mobile sensor networks, the network topology changes frequently. Generally, there is no stable communication path between source node and sink node, even in a short interval. Thus the data forwarding protocol encounters great challenges in mobile sensor networks. Receiver based routing protocols do not require to establish an overall routing from source node to sink node, but allow the neighbor nodes of the sender to contend for the forwarding right under certain rules, and only the contention winner has the opportunity to forward data. Existing receiver based routing protocols can be applied to mobile sensor networks, but they still suffer to some disadvantages in the fields of forwarding priority calculation, data collision, multicast suppression and routing void bypass. Taking into account the characteristics of mobile sensor networks, a receiver based cross-layer forwarding protocol (RCF) is proposed in this paper. The RCF protocol optimizes the forwarding priority calculation and utilizes a self-adaptive mechanism for forwarding right contention. Also, it deals with the data collision and multicast suppression better through the dual-channel communication model, and puts forward an efficient routing void bypass mechanism. The simulation results show that RCF performs well in terms of communication consumption, forwarding delay and reliability.

关键词： Routing protocols

Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

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arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

Spectral-spatial anomaly detection of hyperspectral data based on improved isolation forest

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TechRxiv

TechRxiv 2021年

作者： Song, Xiangyu Aryal, Sunil Ting, Kai Ming Liu, Zhen He, Bin Changchun130033 China The University of Chinese Academy of Sciences Beijing100049 China The School of Information Technology Deakin University 75 Pigdons Rd Waurn Ponds VIC3216 Australia National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Big Data Research Center and Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Anomaly detection in hyperspectral image is affected by redundant bands and the limited utilization capacity of spectral-spatial information. In this article, we propose a novel Improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than the background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Further, we propose a spectral-spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the Relative Mass Isolation Forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the Entropy Rate Segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral data sets demonstrate that the proposed detector outperforms other state-of-the-art methods. © 2021, CC BY.

关键词： Anomaly detection

Improving Heterogeneous Model Reuse by Density Estimation

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arXiv 2023年

作者： Tang, Anke Luo, Yong Hu, Han He, Fengxiang Su, Kehua Du, Bo Chen, Yixin Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Hubei Luojia Laboratory Wuhan China School of Information and Electronics Beijing Institute of Technology China JD Explore Academy *** Inc. China Department of CSE Washington University in St. Louis United States The University of Sydney Australia

This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been developed. However, although pre-trained local classifiers are utilized in these approaches, the characteristics of the local data are not well exploited. This motivates us to estimate the density of local data and design an auxiliary model together with the local classifiers for reuse. To address the scenarios where some local models are not well pre-trained, we further design a multiparty cross-entropy loss for calibration. Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision theory