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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jie Zeng Guoxian Yu Jun Wang Maozu Guo Xiangliang Zhang College of Computer and Information Science Southwest University Chongqing China CEMSE division KAUST Computational Bioscience Research Center Thuwal SA School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9781728118680

Alternative splicing modulates protein-protein and other ligand interactions, it results in proteoforms, translated from isoforms that are alternatively spliced from the same gene, to interact with different partners and have distinct or even opposing functions. Therefore, systematically identifying protein-protein interaction at the isoform-level is crucial to explore the function of proteoforms. Constructing the isoform-level interaction network currently is prohibited by the lack of a large golden set of experimentally validated interacting isoforms, which enable computationally predicting isoform-isoform interactions. In this paper, a deep convolution neural network based multi-instance learning approach called DMIL-III is proposed to predict isoform interactions. DMIL-III takes a gene pair as `bag' and two isoforms of the pairwise genes as the `instance' of the bag. DMIL-III follows the principle of multi-instance learning that at least one isoform-isoform interaction exists for a positive gene pair and none interacting isoforms occurs for a negative gene pair. DMIL-III integrates RNA-seq, nucleotide sequence, domain-domain interaction and exon array data. Experimental results indicate that DMIL-III achieves a superior performance with Accuracy of 93% on single-instance gene bags and of 94% on multi-instance gene bags, which are at least 14% and 29% higher than those of state-of-the-art methods. In addition, we further test DMIL-III on a set of experimentally confirmed isoform-isoform interactions and obtain an Accuracy of 65%, which is at least 10% higher than those of comparing methods at the isoform-level. All these results show the effectiveness of DMIL-III for predicting isoform-isoform interactions.

关键词： biochemistry biology computing data analysis genetics learning (artificial intelligence) molecular biophysics neural nets proteins RNA biology computing molecular biology Neural network biochemistry Data analysis PURSE Genetics Protein Isoforms RNA Bag device Alternative Splicing Protein-Protein Interaction Proteins Genes Statistical analysis

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Ranking-based deep cross-modal hashing

arXiv

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arXiv 2019年

作者： Liu, Xuanwu Yu, Guoxian Domeniconi, Carlotta Wang, Jun Ren, Yazhou Guo, Maozu College of Computer and Information Sciences Southwest University Chongqing China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China Department of Computer Science George Mason University Fairfax United States SMILE Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashing methods are mainly designed to handle simple pairwise similarity. The complex multilevel ranking semantic structure of instances associated with multiple labels has not been well explored yet. In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH firstly uses the feature and label information of data to derive a semi-supervised semantic ranking list. Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions. Experiments on real multi-modal datasets show that RDCMH outperforms other competitive baselines and achieves the state-of-the-art performance in cross-modal retrieval applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Optimization of UAS-based high-throughput phenotyping to estimate plant health and grain yield in sorghum

Plant Phenome Journal

引用

Plant Phenome Journal 2020年第1期3卷

作者： Galli, Giovanni Horne, David W. Collins, S. Delroy Jung, Jinha Chang, Anjin Fritsche-Neto, Roberto Rooney, William L. Dep. of Genetics “Luiz de Queiroz” College of Agriculture Univ. of São Paulo Piracicaba 13418-900 SP Brazil Dep. of Soil and Crop Sciences Texas A&M Univ. College Station 77843 TX United States Lyles School of Civil Engineering Purdue Univ. West Lafayette 47907 IN United States Dep. of Engineering and Computer Science Texas A&M Univ. System Corpus Christi 77840 TX United States

High-throughput phenotyping (HTP) has enabled the acquisition of vast amounts of data. Therefore, finding the most informative phenological stage(s) and high-throughput traits could lead to significant optimization of HTP-assisted selection. An investigation as to when phenotypic data should be collected and how it should be processed from unmanned aerial system (UAS) imagery for the optimization and assessment of two primary traits in grain sorghum [Sorghum bicolor (L). Moench], namely, grain yield and plant health (based on anthracnose scores) was conducted. By evaluating multiple flight dates across the growing season via multispectral UAS-based imagery, a set of scenarios composed of combinations of flight dates and vegetation indices were constructed for analysis. In this sense, results showed no increase in predictive ability when combining multiple vegetation indices. Hence, using only an index with a higher predictive ability (e.g., normalized difference vegetation index (NDVI) or modified simple ratio (MSR) for plant health with 0.75;and any tested index but chlorophyll index (CIg) for grain yield with ∼0.55) is recommended. Likewise, the combining of multiple flights did not result in a significant increase in predictive ability for either primary trait. Thus, we observed that a single flight for each trait (e.g., 121 d after sowing with 0.81 for plant health;104 d after sowing with 0.59 for grain yield) was optimal. Concerning, the predictive algorithms examined, partial least squares regression (PLSR) and neural network, results were similar, with PLSR generally outperforming. In addition, we discuss our findings from an application standpoint of a field-based breeding program and suggest additional optimization options. © 2020 The Authors. The Plant Phenome Journal published by Wiley Periodicals LLC on behalf of American Society of Agronomy and Crop science Society of America

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The Existence of Consensus Equilibria for Data Trading under the Framework of Internet of Things (IoT) with Blockchain Ecosystems

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Procedia computer science 2020年 174卷 55-65页

作者： Lan Di George Xianzhi Yuan Tu Zeng Qianyou Zhang Xiaojing Zhang School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Business School Chengdu University Chengdu 610106 China School of Fintech Shanghai Lixin Univ. of Accounting and Finance Shanghai 201209 China Center for Financial Engineering Soochow University Suzhou 215008 China Business School Sun Yat-sen University Guangzhou 510275 China BBD Technology Co. Ltd.(BBD) No.966-#9 Builiding Tianfu Avenue Chengdu 610093 China Military Science Press Military Academy of Science Xianghongqi Haididan District Beijing 100091 China

The goal of this paper is to use a new notion called “Consensus Game (CG)” with motivation from the mechanism design of blockchain economy under the consensus incentives from Bitcoin ecosystems in financial technology (Fintech) to study the existence of equilibria for the data trading under the framework of Internet of Things (IoT). By a fact that for a given sets of sensors, sensors may take action in cooperative, or noncooperative strategies (games), we formulate s model (called “Consensus Game of IoT”) to analyze this competitive situation among the sensors with the mixture of cooperative and noncooperative strategy (game). Then we provide the analytical condition for the existence of the consensus games of IoT in general. The application shows that the concept of consensus games of IoT may play a key role for the study of data trading under the framework of IoT associated with Blockchain ecosystems.

关键词： Consensus equilibrium 5G Crowdsensing Internet of Things (IoT) Data Trading Nakamoto consensus Bitcoin ecosystem

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Monitoring macroplastics in aquatic and terrestrial ecosystems: Expert survey reveals visual and drone-based census as most effective techniques

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science of the Total Environment 2024年 955卷 176528页

作者： Gallitelli, L. Girard, P. Andriolo, U. Liro, M. Suaria, G. Martin, C. Lusher, A.L. Hancke, K. Blettler, M.C.M. Garcia-Garin, O. Napper, I.E. Corbari, L. Cózar, A. Morales-Caselles, C. González-Fernández, D. Gasperi, J. Giarrizzo, T. Cesarini, G. De, K. Constant, M. Koutalakis, P. Gonçalves, G. Sharma, P. Gundogdu, S. Kumar, R. Garello, N.A. Camargo, A.L.G. Topouzelis, K. Galgani, F. Royer, S.J. Zaimes, G.N. Rotta, F. Lavender, S. Nava, V. Castro-Jiménez, J. Mani, T. Crosti, R. Azevedo-Santos, V.M. Bessa, F. Tramoy, R. Costa, M.F. Corbau, C. Montanari, A. Battisti, C. Scalici, M. Department of Sciences University Roma Tre Viale Guglielmo Marconi 446 Rome00146 Italy Biosciences Institute Federal University of Mato Grosso MT Cuiabá78060-900 Brazil INESC Coimbra Department of Electrical and Computer Engineering Polo 2 Coimbra3030-290 Portugal Institute of Nature Conservation Polish Academy of Sciences al. Adama Mickiewicza 33 Kraków31–120 Poland Istituto di Scienze Marine - Consiglio Nazionale delle Ricerche CNR-ISMAR Pozzuolo di Lerici La Spezia Italy Thuwal Saudi Arabia Oslo Norway Ciudad Universitaria Santa Fe3000 Argentina Faculty of Biology Universitat de Barcelona Barcelona08028 Spain International Marine Litter Research Unit University of Plymouth Plymouth United Kingdom School of Biological and Marine Sciences University of Plymouth Plymouth United Kingdom Dipartimento di Ingegneria Università degli Studi di Palermo Palermo Italy Department of Biology University Marine Research Institute INMAR University of Cádiz and European University of the Seas SEA-EU Puerto Real Spain Univ Gustave Eiffel GERS-EE Campus Nantes France Fortaleza Brazil Corso Tonolli 50 Verbania Pallanza28922 Italy Biological Oceanography Division CSIR- National Institute of Oceanography Dona Paula Goa403004 India Univ. Lille Institut Mines-Télécom Univ. Artois Junia ULR 4515 - LGCgE Laboratoire de Génie Civil et géo-Environnement LilleF-59000 France Department of Forestry and Natural Environment Science International Hellenic University University Campus in Drama Drama66100 Greece University of Coimbra Department of Mathematics Coimbra Portugal Department of Agricultural Engineering and Technology School of Engineering and Technology Nagaland University Nagaland Dimapur India Cukurova University Department of Basic Science Adana Turkey Department of Biosystems Engineering Auburn University AuburnAL36849 United States Cuiabá Brazil Department of Marine Sciences University of Aegean Greece ECHOS D'OCEANS Corse

Anthropogenic litter, such as plastic, is investigated by the global scientific community from various fields employing diverse techniques. The goal is to assess and finally mitigate the pollutants' impacts on the natural environment. Plastic litter can accumulate in different matrices of aquatic and terrestrial ecosystems, impacting both biota and ecosystem functioning. Detection and quantification of macroplastics, and other litter, can be realized by jointly using visual census and remote sensing techniques. The primary objective of this research was to identify the most effective approach for monitoring macroplastic litter in riverine and marine environments through a comprehensive survey based on the experiences of the scientific community. Researchers involved in plastic pollution evaluated four litter occurrence and flux investigation methods (visual census, drone-based surveys, satellite imagery, and GPS/GNSS trackers) through a questionnaire. Traditional visual census and drone deployment were deemed as the most popular approaches among the 46 surveyed researchers, while satellite imagery and GPS/GNSS trackers received lower scores due to limited field validation and short performance ranges, respectively. On a scale from 0 to 5, visual census and drone-based surveys obtained 3.5 and 2.0, respectively, whereas satellite imagery and alternative solutions received scores lower than 1.2. Visual and drone censuses were used in high, medium and low-income countries, while satellite census and GPS/GNSS trackers were mostly used in high-income countries. This work provides an overview of the advantages and drawbacks of litter investigation techniques, contributing i) to the global harmonization of macroplastic litter monitoring and ii) providing a starting point for researchers and water managers approaching this topic. This work supports the selection and design of reliable and cost-effective monitoring approaches to mitigate the ambiguity in macroplastic data co

关键词： Macroinvertebrates

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A java code protection scheme via dynamic recovering runtime instructions 18th

A java code protection scheme via dynamic recovering runtime...

引用

18th International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2018

作者： Jiajia, Sun Jinbao, Gao Yu-an, Tan Yu, Zhang Xiao, Yu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China China University of Mining and Technology Beijing100083 China Department of Computer Science and Technology Shandong University of Technology ZiboShandong255022 China

ISBN: (纸本)9783030050627

As Android operating system and applications on the device play important roles, the security requirements of Android applications increased as well. With the upgrade of Android system, Android runtime mode (ART mode) has gradually become the mainstream architecture of the Android operating system. ART introduces several improvements in Android, but it also introduces new ways to enhance malicious activities. This paper proposed a confidential finer granularity protection scheme for application programs under ART mode of ROOT Android devices. Taking Java method as the protection granularity, the protection scheme increased the accuracy of protecting targets. In addition, the protection scheme provided a more thorough protection for applications by combining dynamic loading technology and encryption technology in ART mode, and improved the security of Android applications. Experiments showed that the proposed protection scheme is effective. © Springer Nature Switzerland AG 2018.

关键词： Android (operating system)

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Multiple independent subspace clusterings

arXiv

引用

arXiv 2019年

作者： Wang, Xing Wang, Jun Domeniconi, Carlotta Yu, Guoxian Xiao, Guoqiang Guo, Maozu College of Computer and Information Sciences Southwest University Chongqing China Department of Computer Science George Mason University FairfaxVA United States Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Hubei China School of Electrical and Information Engineering Beijing University Of Civil Engineering and Architecture Beijing China

Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it's still a challenge for users to analyze and understand the distinctive structure of each output clustering. To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering. To this end, we provide a two-stage approach called MISC (Multiple Independent Subspace Clusterings). In the first stage, MISC uses independent subspace analysis to seek multiple and statistical independent (i.e. non-redundant) subspaces, and determines the number of subspaces via the minimum description length principle. In the second stage, to account for the intrinsic geometric structure of samples embedded in each subspace, MISC performs graph regularized semi-nonnegative matrix factorization to explore clusters. It additionally integrates the kernel trick into matrix factorization to handle non-linearly separable clusters. Experimental results on synthetic datasets show that MISC can find different interesting clusterings from the sought independent subspaces, and it also outperforms other related and competitive approaches on real-world datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Clustering algorithms

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Evaluation of Vehicle-Road Suitability Analysis on Highway by Accident or Comparison 18

Evaluation of Vehicle-Road Suitability Analysis on Highway b...

引用

18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018

作者： Hou, Shu-Zhan He, Yu-Long Bao, Xing-Jian Cui, Na School of Civil Engineering and Architecture Univ. of Jinan Jinan Shandong250022 China College of Metropolitan Transportation Beijing Univ. of Technology Beijing100124 China Xi'An City Planning and Design Institute Xi'an Shaanxi710082 China

ISBN: (数字)9780784481523

ISBN: (纸本)9780784481523

Both vehicles and roads are important elements of a roadway transportation system. Vehicles should run in coordination with roads when moving goods or passengers. Good suitability of vehicle-road is a necessary condition of transport operating safety. However, there is a lack of research for evaluating this suitability. This article analyzes a dataset of statistical traffic accidents on highways in China from 2010 to 2014. Then, it studies the quantitative description of the suitability of vehicle-road with respect to traffic safety. As a result, an evaluation method is developed for multitype vehicles, using accident odds ratios comparison. The main finding is that trucks have an accident odds ratio four times greater than passenger vehicles. Conclusions will provide support for how to coordinate the relationship between vehicle and road in order to improve vehicle running safety. © 2018 American Society of civil Engineers.

关键词： Vehicles

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How the growth of ice depends on the fluid dynamics underneath

arXiv

引用

arXiv 2020年

作者： Wang, Ziqi Calzavarini, Enrico Sun, Chao Toschi, Federico Center for Combustion Energy Key Laboratory for Thermal Science and Power Engineering Ministry of Education Department of Energy and Power Engineering Tsinghua University Beijing100084 China Univ. Lille Unité de Mécanique de Lille - J. Boussinesq - UML ULR 7512 LilleF-59000 France Department of Engineering Mechanics School of Aerospace Engineering Tsinghua University Beijing100084 China Department of Physics Department of Mathematics and Computer Science J. M. Burgers Centre for Fluid Dynamics Eindhoven University of Technology Eindhoven5600 MB Netherlands CNR-IAC Via dei Taurini 19 Rome00185 Italy

Convective flows coupled with solidification or melting in water bodies play a major role in shaping geophysical landscapes. Particularly in relation to the global climate warming scenario, it is essential to be able to accurately quantify how water-body environments dynamically interplay with ice formation or melting process. Previous studies have revealed the complex nature of the icing process, but have often ignored one of the most remarkable particularity of water, its density anomaly, and the induced stratification layers interacting and coupling in a complex way in presence of turbulence and phase change. By combining experiments, numerical simulations, and theoretical modeling, we investigate solidification of freshwater, properly considering phase transition, water density anomaly, and real physical properties of ice and water phases, which we show to be essential for correctly predicting the different qualitative and quantitative behaviors. We identify, with increasing thermal driving, four distinct flow-dynamics regimes, where different levels of coupling among ice front, stably and unstably stratified water layers occur. Despite the complex interaction between the ice front and fluid motions, remarkably, the average ice thickness and growth rate can be well captured with the theoretical model. It is revealed that the thermal driving has major effects on the temporal evolution of the global icing process, which can vary from a few days to a few hours in the current parameter regime. Our model can be applied to general situations where the icing dynamics occurs under different thermal and geometrical conditions (e.g. cooling conditions or water layer depth). Copyright © 2020, The Authors. All rights reserved.

关键词： Ice

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Multi-view multi-instance multi-label learning based on collaborative matrix factorization

arXiv

引用

arXiv 2019年

作者： Xing, Yuying Yu, Guoxian Domeniconi, Carlotta Wang, Jun Zhang, Zili Guo, Maozu College of Computer and Information Science Southwest University Chongqing China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China Department of Computer Science George Mason University FairfaxVA United States School of Information Technology Deakin University GeelongVIC Australia School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China

Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance. In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instances, and labels, to encode different types of relations via multiple relational data matrices. To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices. An aggregation scheme is further introduced to aggregate the instance-level labels into bag-level and to guide the factorization. An empirical study on benchmark datasets show that M3Lcmf outperforms other related competitive solutions both in the instance-level and bag-level prediction. Copyright © 2019, The Authors. All rights reserved.

关键词： Matrix factorization

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