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Spatial resolution for forest carbon maps

science (New York, N.Y.) 2025年第6732期387卷 370-371页

作者： Laura Duncanson Neha Hunka Tommaso Jucker John Armston Nancy Harris Lola Fatoyinbo Christopher A Williams Jeff W Atkins Brett Raczka Shawn Serbin Michael Keller Ralph Dubayah Chad Babcock Mark A Cochrane Andrew Hudak George C Hurtt Paul M Montesano L Monika Moskal Taejin Park Sassan Saatchi Carlos A Silva Hao Tang Rodrigo Vargas Aaron Weiskittel Konrad Wessels Scott J Goetz Geographical Sciences University of Maryland College Park MD USA. School of Biological Sciences University of Bristol Bristol UK. World Resources Institute Washington DC USA. NASA Goddard Space Flight Center Greenbelt MD USA. Graduate School of Geography Clark University Worcester MA USA. US Department of Agriculture (USDA) Forest Service Southern Research Station New Ellenton SC USA. Department of Biology Virginia Commonwealth University Richmond VA USA. Computational and Information Systems Lab Data Assimilation Research Section National Center for Atmospheric Research Boulder CO USA. International Institute of Tropical Forestry USDA Forest Service Rio Piedras PR USA. Forest Resources Department University of Minnesota St. Paul MN USA. University of Maryland Center for Environmental Science Frostburg MD USA. Rocky Mountain Research Station USDA Forest Service Moscow ID USA. NASA Goddard Space Flight Center ADNET Systems Greenbelt MD USA. School of Environmental and Forest Sciences University of Washington WA USA. Bay Area Environmental Research Institute Moffett Field CA USA. NASA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA. CTrees Pasadena CA USA. Forest Biometrics Remote Sensing and Artificial Intelligence Lab (SilvaLab) School of Forest Fisheries and Geomatics Science University of Florida Gainesville FL USA. Department of Geography National University of Singapore Singapore. Department of Plant and Soil Sciences University of Delaware Newark DE USA. Center for Research on Sustainable Forests University of Maine Orono ME USA. Geography and Geoinformation Science George Mason University Fairfax VA USA. School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA.

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Feature Location Benchmark for Decomposing and Reusing Android Apps

arXiv

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arXiv 2020年

作者： Tang, Yutian Zhou, Hao Xu, Zhou Luo, Xiapu Cai, Yan Zhang, Tao Department of Computing Hong Kong Polytechnic University Hong Kong School of Big Data and Software Engineering Chongqing University China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Faculty of Information Technology Macau University of Science and Technology China

Software reuse enables developers to reuse architecture, programs and other software artifacts. Realizing a systematical reuse in software brings a large amount of benefits for stakeholders, including lower maintenance efforts, lower development costs, and time to market. Unfortunately, currently implementing a framework for large-scale software reuse in Android apps is still a huge problem, regarding the complexity of the task and lacking of practical technical support from either tools or domain experts. Therefore, proposing a feature location benchmark for apps will help developers either optimize their feature location techniques or reuse the assets created in the benchmark for reusing. In this paper, we release a feature location benchmark, which can be used for those developers, who intend to compose software product lines (SPL) and release reuse in apps. The benchmark not only contributes to the research community for reuse research, but also helps participants in industry for optimizing their architecture and enhancing modularity. In addition, we also develop an Android Studio plugin named caIDE for developers to view and operate on the benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： Computer software reusability

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Hybrid feature selection to identify important single nucleotide polymorphism for asthma classification

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AIP Conference Proceedings 2024年第1期2991卷

作者： Wan Ni Wong Weng Howe Chan Hui Wen Nies Kauthar Mohd Daud Artificial Intelligence and Bioinformatics Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Center for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia

Single nucleotide polymorphism (SNP), being the most common genetic variation, is found to be responsible for traits and complex diseases such as asthma. Polygenic risk scoring (PRS) and machine learning (ML) are the current two common types of genetic risk prediction models. PRS employs statistical association testing to identify alleles with risk. However, it was found not performing well in unraveling the SNP-SNP interactions and attaining low predictive value. In contrast, ML algorithms are more robust in detecting disease associated SNPs, resulting in an improvement in prediction performance. The challenge remains as the high dimensionality nature of SNP tends to cause overfitting problems and impact the classifier performance. This paper introduces a two-stage hybrid feature selection that integrates Mutual Information and Random Forest Recursive Feature Elimination for identifying informative SNPs from 176,287 SNPs in a dataset of 128 asthmatic and non-asthmatic samples. The reduced samples based on the identified informative SNPs are then used with Support Vector Machine to train a classification model. The model from this paper outperforms other ML models with an average accuracy, precision, recall and area under the curve of 98.91%, 98.36%, 99.72% and 0.99 respectively. Additional analysis shows that the genes associated with the top selected SNPs, namely Ring Finger Protein 217 (RNF217) and A-kinase-anchoring protein 6 (AKAP6) are correlated with respiratory diseases. This shows that the proposed method can identify important SNPs that are able to differentiate between asthmatic and non-asthmatic samples.

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Beatgan: Anomalous rhythm detection using adversarially generated time series 28

Beatgan: Anomalous rhythm detection using adversarially gene...

引用

28th International Joint Conference on Artificial Intelligence, IJCAI 2019

作者： Zhou, Bin Liu, Shenghua Hooi, Bryan Cheng, Xueqi Ye, Jing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science National University of Singapore Singapore Department of Anesthesiology Nanfang Hospital Southern Medical University China CAS Key Laboratory of Network Data Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780999241141

Given a large-scale rhythmic time series containing mostly normal data segments (or 'beats'), can we learn how to detect anomalous beats in an effective yet efficient way? For example, how can we detect anomalous beats from electrocardiogram (ECG) readings? Existing approaches either require excessively high amounts of labeled and balanced data for classification, or rely on less regularized reconstructions, resulting in lower accuracy in anomaly detection. Therefore, we propose BeatGAN, an unsupervised anomaly detection algorithm for time series data. BeatGAN outputs explainable results to pinpoint the anomalous time ticks of an input beat, by comparing them to adversarially generated beats. Its robustness is guaranteed by its regularization of reconstruction error using an adversarial generation approach, as well as data augmentation using time series warping. Experiments show that BeatGAN accurately and efficiently detects anomalous beats in ECG time series, and routes doctors' attention to anomalous time ticks, achieving accuracy of nearly 0.95 AUC, and very fast inference (2.6 ms per beat). In addition, we show that BeatGAN accurately detects unusual motions from multivariate motion-capture time series data, illustrating its generality. © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.

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DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems

arXiv

引用

arXiv 2020年

作者： Pan, Xiang Chen, Minghua Zhao, Tianyu Low, Steven H. Department of Information Engineering The Chinese University of Hong Kong Hong Kong School of Data Science City University of Hong Kong Hong Kong Department of Computing and Mathematical Sciences Department of Electrical Engineering California Institute of Technology United States

To cope with increasing uncertainty from renewable generation and flexible load, grid operators need to solve alternative current optimal power flow (AC-OPF) problems more frequently for efficient and reliable operation. In this paper, we develop a Deep Neural Network (DNN) approach, called DeepOPF, for solving AC-OPF problems in a fraction of the time used by conventional iterative solvers. A key difficulty for applying machine learning techniques for solving AC-OPF problems lies in ensuring that the obtained solutions respect the equality and inequality physical and operational constraints. Generalized a prediction-and-reconstruction procedure in our previous studies, DeepOPF first trains a DNN model to predict a set of independent operating variables and then directly compute the remaining ones by solving the power flow equations. Such an approach not only preserves the power-flow balance equality constraints but also reduces the number of variables to be predicted by the DNN, cutting down the number of neurons and training data needed. DeepOPF then employs a penalty approach with a zero-order gradient estimation technique in the training process towards guaranteeing the inequality constraints. We also drive a condition for tuning the DNN size according to the desired approximation accuracy, which measures its generalization capability. It provides theoretical justification for using DNN to solve AC-OPF problems. Simulation results for IEEE 30/118/300-bus and a synthetic 2000-bus test cases demonstrate the effectiveness of the penalty approach. They also show that DeepOPF speeds up the computing time by up to two orders of magnitude as compared to a state-of-the-art iterative solver, at the expense of Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Improving the Efficiency of Blockchain Applications with Smart Contract based Cyber-insurance

Improving the Efficiency of Blockchain Applications with Sma...

引用

IEEE International Conference on Communications (ICC)

作者： Jia Xu Yongqi Wu Xiapu Luo Dejun Yang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Computing The Hong Kong Polytechnic University Hongkong China Department of Computer Science Colorado School of Mines Golden USA

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Blockchain based applications benefit from decentralization, data privacy, and anonymity. However, they may suffer from inefficiency due to underlying blockchain. In this paper, we aim to address this limitation while still enjoying the privacy and anonymity. Taking the blockchain based crowdsourcing system as an example, we propose a new smart contract based cyber-insurance framework, which can greatly shorten the delay, and enable the workers to obtain the economic compensation for increased security risk caused by a conflict between the need to provide service quickly and delay in payment. We model the process of determining insurance premium and number of confirmations as a Stackelberg Game and prove the existence of Stackelberg Equilibria, at which the utility of the requester is maximized, and none of the workers can improve its utility by unilaterally deviating from its current strategy. The experimental results show that our framework can definitely improve the time efficiency of crowdsourcing. Particularly, it takes on average only 33% of the time required by the naive blockchain based crowdsouring solution for time-sensitive cases.

关键词： Contracts Crowdsourcing Task analysis Insurance Bitcoin

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IndexIt: Enhancing data locating services for parallel file systems 21

IndexIt: Enhancing data locating services for parallel file ...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data science and Systems, HPCC/SmartCity/DSS 2019

作者： Cheng, Peng Wang, Yong Lu, Yutong Du, Yunfei Chen, Zhiguang College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Changsha China National Supercomputer Center in Guangzhou School of Data and Computer Science Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9781728120584

While the ability to access a small fraction of data records from a large volume of scientific datasets is vital to accelerate scientific discovery, existing parallel file systems face serious challenges in managing scientific big data since data services have traditionally been decoupled from file systems. In this paper, we present IndexIt, an in-situ index and query middleware that aims to enhance record locating services for parallel file systems. When applications are writing data to parallel file systems, IndexIt allows users to index data while they are still in memory and bypass the performance bottleneck between memory and disks. By applying lightweight Bitmap-Range index and organizing the index as key-value pairs, IndexIt accelerates index building efficiently. Moreover, we propose a two-level query processing framework to process query requests. We build a prototype and evaluate its performance with real scientific datasets. Compared with existing data management tools, the index building time can be reduced dramatically. The proposed two-level query processing achieves up to 3 orders of magnitude speed-up than scanning the entire dataset and shows up to 1.35x speed-up than an existing tool. © 2019 IEEE.

关键词： Information management

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Retraction notice to “Fuzzy logic based on-line fault detection and classification method of substation equipment based on convolutional probabilistic neural network with discrete wavelet transform and fuzzy interference” [Optik 270 (2022) 169956]

引用

Optik 2023年 288卷

作者： Romany F. Mansour Eatedal Alabdulkreem Heba F. Eid K. Sathishkumar Mohd Abdul Rahim Khan Anil Kumar Department of Mathematics Faculty of Science New Valley University El-Kharja 72511 Egypt Department of Computer Science College of Computer and Information Science Princess Nourah Bint Abdulrahman University P.O.Box 84428 Riyadh 11671 Saudi Arabia Faculty of Science AL-Azhar University Cairo Egypt Department of Computer Science Gobi Arts & Science College(Autonomous) Gobichettipalayam Tamilnadu India Department of Computer Science College of Computer and Information Sciences Majmaah University Al-Majmaah 11952 Saudi Arabia Data Science Research Group School of Computing DIT University Dehradun India

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Deep Learning Based Valid Bug Reports Determination and Explanation

Deep Learning Based Valid Bug Reports Determination and Expl...

引用

International Symposium on Software Reliability Engineering (ISSRE)

作者： Jianjun He Ling Xu Yuanrui Fan Zhou Xu Meng Yan Yan Lei Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University) Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9781728198705

ISBN: (纸本)9781728198712

Bug reports are widely used by developers to fix bugs. Due to the lack of experience, reporters may submit numerous invalid bug reports. Manually determining valid bug reports is a laborious task. Automatically identifying valid bug reports can save time and effort for bug analysis. In this paper, we propose a deep learning-based approach to determine and explain valid bug reports using only textual information i.e., summaries and descriptions of bug reports. Convolutional neural network (CNN) is applied to capture their contextual and semantic features. Moreover, by analyzing the spatial structure of CNN, we backtrack the trained CNN model to get phrases that can explain valid bug reports determination. After inspecting the phrases manually, we summarize some valid bug report patterns. We evaluate our approach on five large-scale open-source projects containing a total of 540491 bug reports. On average, across the five projects, our approach achieves 0.85, 0.80, 0.69 and improves the state-of-the-art approach by 8.97%, 9.59%, 9.52% in terms of AUC, F1-score for valid bug reports, and F1-score for invalid bug reports, respectively. From the summarized patterns, we can find that determining valid bug reports is mainly due to three categories of patterns: Attachment, Environment, and Reproduce.

关键词： Fans Computer bugs Semantics Feature extraction Software reliability Task analysis Open source software

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Trapdoor Delegation and HIBE from Middle-Product LWE in Standard Model

arXiv

引用

arXiv 2020年

作者： Le, Huy Quoc Duong, Dung Hoang Susilo, Willy Pieprzyk, Josef Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Northfields Avenue WollongongNSW2522 Australia CSIRO Data61 SydneyNSW Australia Institute of Computer Science Polish Academy of Sciences Warsaw Poland

At CRYPTO 2017, Roşca, Sakzad, Stehlé and Steinfeld introduced the Middle–Product LWE (MPLWE) assumption which is as secure as Polynomial-LWE for a large class of polynomials, making the corresponding cryptographic schemes more flexible in choosing the underlying polynomial ring in design while still keeping the equivalent efficiency. Recently at TCC 2019, Lombardi, Vaikuntanathan and Vuong introduced a variant of MPLWE assumption and constructed the first IBE scheme based on MPLWE. Their core technique is to construct lattice trapdoors compatible with MPLWE in the same paradigm of Gentry, Peikert and Vaikuntanathan at STOC 2008. However, their method cannot directly offer a Hierachical IBE construction. In this paper, we make a step further by proposing a novel trapdoor delegation mechanism for an extended family of polynomials from which we construct, for the first time, a Hierachical IBE scheme from MPLWE. Our Hierachy IBE scheme is provably secure in the standard model. Copyright © 2020, The Authors. All rights reserved.

关键词： Polynomials

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