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32nd AAAI Conference on Artificial Intelligence, AAAI 2018

作者： Zhang, Ao Li, Nan Pu, Jian Wang, Jun Yan, Junchi Zha, Hongyuan Shanghai Key Laboratory of Trustworthy Computing MOE International Joint Lab of Trustworthy Software School of Computer Science and Software Engineering East China Normal University Shanghai China Institute of Data Science and Technologies Alibaba Group Hangzhou China IBM Research China Georgia Institute of Technology Atlante United States

ISBN: (纸本)9781577358008

In many real-world applications, learning a classifier with false-positive rate under a specified tolerance is appealing. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which are of limitation in methodology since they do not directly incorporate the false-positive rate tolerance. In this paper, we propose a novel scoring-thresholding approach, τ-False Positive Learning (τ-FPL) to address this problem. We show that the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ-FPL over the existing approaches. Copyright © 2018, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mathematical transformations

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An Advanced Quantum-Resistant Signature Scheme for Cloud Based on Eisenstein Ring

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Computers, Materials & Continua 2018年第7期56卷 19-34页

作者： Faguo Wu Xiao Zhang Wang Yao Zhiming Zheng Lipeng Xiang Wanpeng Li Key Laboratory of Mathematics Informatics and Behavioral SemanticsMinistry of EducationSchool of Mathematics and Systems ScienceBeihang UniversityBeijing100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100191China The Fifth Electronics Research Institute of Ministry of Industry and Information Technology Guangzhou510610China Department of Electrical and Electronic Engineering City University of LondonUK

Signature,widely used in cloud environment,describes the work as readily identifying its *** existing signature schemes in the literature mostly rely on the Hardness assumption which can be easily solved by quantum *** this paper,we proposed an advanced quantum-resistant signature scheme for Cloud based on Eisenstein Ring(ETRUS)which ensures our signature scheme proceed in a lattice with higher *** proved that ETRUS highly improve the performance of traditional lattice signature ***,the Norm of polynomials decreases significantly in ETRUS which can effectively reduce the amount of polynomials convolution ***,storage complexity of ETRUS is smaller than classical ***,according to all convolution of ETRUS enjoy lower degree polynomials,our scheme appropriately accelerate 56.37%speed without reducing its security level.

关键词： Signature quantum-resistant Eisenstein Ring ETRUS

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Detection of Pathological Condition of Heart Using Texture Complexity of the Heart Sound Signals in Kernel Space

Detection of Pathological Condition of Heart Using Texture C...

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International Conference on BioSignal Analysis, Processing and Systems (ICBAPS)

作者： Ashok Mondal Ramaswamy Palaniappan National Institute of Technology Karnataka India Data Science Research Group School of Computing University of Kent UK

The heart sound signal is produced due to the mechanical activity of cardiovascular system and hence, it carries relevant information regarding the pathological and non-pathological condition of heart. Physicians listen to heart sounds using a stethoscope device and make interpretation regarding the diseases if any. Hence, its performance depends on the medical practitioners knowledge and experience. In this study, a very simple alternative approach of auscultation technique is introduced for early detection of cardiac abnormality based on a combined framework of kernel space decomposition and textural analysis. The experimental results are validated by physician's diagnosis and statistical analysis. The proposed diagnostic tool can be used in combination with audio clue of heart sound signal to train inexperienced physicians and as an assistive device for experienced doctors.

关键词： Heart Feature extraction Entropy Support vector machines Wavelet transforms Diseases

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Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

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SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Aligning satellite-based phenology in a deep learning model for improved crop yield estimates over large regions

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Agricultural and Forest Meteorology 2025年 372卷

作者： Zhang, Jiaying Guan, Kaiyu Chen, Zhangliang Hipple, James Huang, Yizhi Peng, Bin Wang, Sibo Xu, Xiangtao Jin, Zhenong Zhao, Kejie Jong, Maxwell Agroecosystem Sustainability Center Institute for Sustainability Energy and Environment University of Illinois Urbana-Champaign Urbana 61801 IL United States Department of Natural Resources and Environmental Sciences College of Agricultural Consumers and Environmental Sciences University of Illinois Urbana-Champaign Urbana 61801 IL United States National Center for Supercomputing Applications University of Illinois Urbana-Champaign Urbana 61801 IL United States Siebel School of Computing and Data Science University of Illinois Urbana-Champaign Urbana IL United States Risk Management Agency United States Department of Agriculture Washington 20250 DC United States Department of Ecology and Evolutionary Biology Cornell University Ithaca 14853 NY United States Department of Bioproducts and Biosystems Engineering University of Minnesota St. Paul 55108 MN United States

Accurate estimate of crop yield is crucial for agricultural planning and resource allocation. Crop yield estimation over large regions is especially challenging because of large variations of crop phenology and environmental conditions. Environmental stresses, such as drought and heat, during different phenological stages impose different impacts on plant growth and thus on final grain yield. We hypothesize that incorporating the phenology information, specifically, aligning environmental conditions with plant phenological timing, can better simulate the impact of environmental conditions on crop yield and thus improve crop yield estimates. To test our hypothesis, we build a deep learning model to predict crop yield using growing-season weather and satellite time series that are either aligned with field-level phenology or not. Based on rainfed corn fields in the 12 states in the US Midwest between 2011 and 2020, we show that applying the aligned phenology in the deep learning model decreases the error of corn yield estimates from 34.1 bu/ac (or 21.8 %) to 29.2 bu/ac (or 18.7 %) and increase the explained yield variability from 61 % to 71 % for leave-one-year-out predictions. The improvements are most significant in fields with yield loss that are associated with early phenological timing. Specifically, in the extreme drought year 2012, the phenology was ∼20 days earlier than normal years, aligning the input sequences with field-level phenological timing captures 78 % of the yield loss, reduces the error by 15 %, and increases the explained variability over 50 %. Our results demonstrate the importance of aligned phenological timing for capturing environmental stress in crop yield estimates and show great potential of phenology alignment in accurate crop yield estimates over large regions. © 2025 The Authors

关键词： Crop phenology Crop yield estimates Deep learning Satellite Yield loss

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Spatially-resolved spectro-photometric SED Modeling of NGC 253’s Central Molecular Zone I. Studying the star formation in extragalactic giant molecular clouds

arXiv

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arXiv 2025年

作者： Humire, Pedro K. Dey, Subhrata Ronconi, Tommaso Sasse, Victor H. Fernandes, Roberto Cid Martín, Sergio Donevski, Darko Malek, Katarzyna Fernández-Ontiveros, Juan A. Song, Yiqing Hamed, Mahmoud Mangum, Jeffrey G. Henkel, Christian Rivilla, Víctor M. Colzi, Laura Harada, N. Demarco, Ricardo Goyal, Arti Meier, David S. Panda, Swayamtrupta Krabbe, Ângela C. Yan, Yaoting Lopes, Amanda R. Sakamoto, K. Muller, S. Tanaka, K. Yoshimura, Y. Nakanishi, K. Kanaan, Antonio Ribeiro, Tiago Schoenell, William de Oliveira, Claudia Mendes Departamento de Astronomia Instituto de Astronomia Geofísica e Ciências Atmosféricas da USP Cidade Universitária SP São Paulo05508-090 Brazil Astronomical Observatory The Jagiellonian University Orla 171 Kraków30-244 Poland National Center for Nuclear Research ul. Pasteura 7 Warsaw02-093 Poland Via Bonomea 265 TriesteIT-34136 Italy Via Beirut 2 TriesteIT-34151 Italy Via Gobetti 93/3 BolognaI-40127 Italy ICSC – Centro Nazionale di Ricerca in High Performance Computing Big Data e Quantum Computing Via Magnanelli 2 Bologna Italy Universidade Federal de Santa Catarina Campus Universitário Reitor João David Ferreira Lima SC Florianópolis88040-900 Brazil European Southern Observatory Alonso de Córdova 3107 Vitacura Santiago763-0355 Chile Joint ALMA Observatory Alonso de Córdova 3107 Vitacura Santiago763-0355 Chile Plaza San Juan 1 TeruelE–44001 Spain CSIC-INTA Carretera de Ajalvir km 4 Torrejón de Ardoz Madrid28850 Spain National Radio Astronomy Observatory 520 Edgemont Road CharlottesvilleVA22903-2475 United States Max-Planck-Institut für Radioastronomie Auf-dem-Hügel 69 Bonn53121 Germany Xinjiang Astronomical Observatory Chinese Academy of Sciences Urumqi830011 China National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Institute of Astronomy and Astrophysics Academia Sinica 11F of AS/NTU Astronomy-Mathematics Building No.1 Sec. 4 Roosevelt Rd Taipei106319 Taiwan 2-21-1 Osawa Mitaka Tokyo181-1855 Japan Institute of Astrophysics Facultad de Ciencias Exactas Universidad Andrés Bello Sede Concepción Talcahuano Chile New Mexico Institute of Mining and Technology 801 Leroy Place SocorroNM87801 United States International Gemini Observatory NSF NOIRLab Casilla 603 La Serena Chile Instituto de Astrofísica de La Plata CONICET-UNLP Paseo del Bosque s/n B1900FWA Argentina Department of Space Earth and Environment Chalmers University of Technology Onsala Space Observatory OnsalaSE-4

Context. Studying the interstellar medium in nearby starbursts is essential for gaining insights into the physical mechanisms driving these extreme objects, thought to be analogs of young, primeval, star-forming galaxies. This task is now feasible due to deep spectro-photometric data enabled by rapid advancements in ground- and space-based facilities. To fully leverage this wealth of information, extracting insights from the spectral line properties, and the spectral energy distribution (SED) is imperative. Aims. This study aims to produce and analyze the physical properties of the first spatially-resolved multi-wavelength SED of an extragalactic source that covers six decades in frequency (from near-ultraviolet to centimeter wavelengths) at an angular resolution of 3′′ which corresponds to linear scale of ∼51 pc at the distance of NGC 253. We focus on the central molecular zone (CMZ) of this starburst galaxy, which contains giant molecular clouds (GMCs) responsible for half of the galaxy’s star formation. Methods. We retrieve archival data from optical to centimeter wavelengths, covering six decades of spectral range. We compute SEDs to fit the observations using the GalaPy code and confronting results with the CIGALE code for validation. We also employ starlight library to analyze the stellar optical spectra of the GMCs. Results. Our results reveal significant differences between central and external GMCs in terms of stellar and dust masses, star formation rates (SFRs), and bolometric luminosities, to provide a few. We have obtained tight relations between monochromatic stellar tracers and star-forming conditions obtained from panchromatic emission. We find that the best SFR tracers are radio continuum bands at 33 GHz, radio recombination lines (RRLs), and the total infrared (IR) luminosity range (LIR;8–1000µm) as well as the IR emission at 60µm. The emission line diagnostics based on the BPT and WHAN diagrams suggest that the nuclear region of NGC 253 exhibits sh

关键词： Optical emission spectroscopy

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Multi-Thread Concurrent Compression Algorithm for Genomic Big data

Multi-Thread Concurrent Compression Algorithm for Genomic Bi...

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IEEE International Conference on Parallel and Distributed computing, Applications and Technologies (PDCAT)

作者： Yimu Ji Shuai Chen Haichang Yao Houzhi Fang Kui Li Shangdong Liu Zhengyuan Xie Kairui Wang School of Computer Science Nanjing University of Posts and Telecommunications Institute of High Performance Computing and Big Data Nanjing University of Posts and Telecommunications Nanjing Center of HPC China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications School of Computer and Software Nanjing Institute of Industry Technology Nanjing China University of Toronto Toronto Canada BSc Mathematics with Finance University of Liverpool Liverpool United Kingdom

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

At present, there are many excellent genome compression algorithms with high genome compression ratio. However, there is a lack of highly efficient compression algorithms for simultaneous compression of a large number of genomes. This manuscript presents an algorithm, which is called FastLNGC, for simultaneous compression of a large amount of genome data based on multi-thread concurrency. This algorithm is based on the LNGC (Large Number of Genomes Compressor) algorithm, and adopts multi-thread technology to achieve concurrent processing of genome data compression. A large number of experiments show that FastLNGC has better performance on compression of a large number of genes. The source code of FastLNGC is available at https://***/APandaThief/FastLNGC.

关键词： Bioinformatics Genomics Compression algorithms Instruction sets Telecommunications Computer science Concurrent computing

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A blockchain-based self-tallying voting scheme in decentralized IoT

arXiv

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arXiv 2019年

作者： Li, Yannan Susilo, Willy Yang, Guomin Yu, Yong Liu, Dongxi Guizani, Mohsen Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia School of Computer Science Shaanxi Normal University Xi’an710062 China Data61 CSIRO MarsfieldNSW2122 Australia Dept. of Electrical and Computer Engineering University of Idaho MoscowID United States

The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. Most of the existing IoT infrastructures are centralized, in which the presence of a cloud server is mandatory. However, centralized frameworks suffer from the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology of Blockchain. Voting systems are widely adopted in IoT, such as a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to traditional centralized voting systems in decentralized IoT since the traditional ones are not suitable for such scenarios. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused by malicious voters. In this paper, we introduce a framework of self-tallying systems in decentralized IoT based on Blockchain. We propose a concrete construction and prove the proposed system satisfies all the security requirements including fairness, dispute-freeness and maximal ballot secrecy. The implementations on mobile phones demonstrate the practicability of our system. Copyright © 2019, The Authors. All rights reserved.

关键词： Internet of things

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Learning Orthodontic Cephalometry through Augmented Reality: A Conceptual Machine Learning Validation Approach 2

Learning Orthodontic Cephalometry through Augmented Reality:...

引用

2nd International Conference on Electrical Engineering and Informatics, ICELTICs 2018

作者： Lakshmana Rao, Gururajaprasad Kaggal Mokhtar, Norehan Iskandar, Yulita Hanum P. Channarayapatna Srinivasa, Arvind Craniofacial and Biomaterial Science Cluster Advanced Medical and Dental Institute Universiti Sains Malaysia Penang13200 Malaysia Graduate School of Busines Universiti Sains Malaysia Penang11800 Malaysia Cognitive Computing and Data Science Research Lab Global Technology Office Cognizant Technology Solutions India

ISBN: (纸本)9781538661406

The orthodontic cephalometry which forms a quintessential radiographic examination provides a valuable diagnostic resource for everyday orthodontic diagnosis and treatment planning. The method of analyzing the lateral cephalometric radiograph (LCR) has been found to lack effective methods of landmark identification and objective validation. This drawback, with the use of augmented reality (AR) for 3D visualization and machine learning for objective validation, can be effectively incorporated into the learning of cephalometrics. The two technologies together can produce a learning platform by reducing subjective errors and its implications on treatment planning in addition to effectively eliminating dissonance in learning. © 2018 IEEE.

关键词： Augmented reality

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Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

Systematic Resource Allocation in Cloud RAN with Caching as ...

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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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