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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Surendra Maharjan Wenzhao Li Shahryar Fazli Hani Sewilam Hesham El-Askary Computational and Data Sciences Graduate Program Schmid College of Science and Technology Chapman University Orange CA USA Earth Systems Science and Data Solutions Lab Chapman University Orange CA USA The Center for Applied Research on the Environment and Sustainability The American University in Cairo Cairo Egypt Department of Engineering Hydrology RWTH Aachen University Aachen Germany Department of Environmental Sciences Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Vapor Pressure Deficit (VPD) is crucial in meteorology and agriculture for understanding plant-environment interactions. Its application as an indicator in agricultural practices notably advances Sustainable Development Goals such as Zero Hunger (SDG 2) and Climate Action (SDG 13). This research focuses on the impact of climate change on agricultural productivity and food security in the Nile River Basin (NRB), emphasizing the role of VPD, temperature, and precipitation. Utilizing Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets from NEX-GDDP-CMIP6, the study analyzes key climatic variables that influence agricultural conditions. The study applies the Mann-Kendall test to evaluate VPD trends from 2000 to 2060 under two Shared Socioeconomic Pathways (SSPs), SSP2-4.5 and SSP5-8.5. The study's findings on the implications of rising VPD levels in the Nile River Basin (NRB), particularly under the SSP 5-8.5 scenario, highlight a critical challenge for the region's agricultural productivity and food security. The increased VPD, indicative of drier conditions, leads to a moisture deficit for crops, potentially reducing agricultural yields. This scenario poses a significant threat to food security, as lower crop yields can result in food shortages and higher food prices, adversely affecting vulnerable populations. The study underscores the necessity of integrating VPD insights into agricultural and water resource management strategies to uphold food security against climatic variations in support of the SDGs.

关键词： Productivity Climate change Precipitation Food security Agriculture Rivers Sustainable development

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Multimodal speech recognition using EEG and audio signals: A novel approach for enhancing ASR systems

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Smart Health 2024年 32卷

作者： Das, Anarghya Soni, Puru Huang, Ming-Chun Lin, Feng Xu, Wenyao Department of Computer Science and Engineering University at Buffalo Buffalo 14261 United States Department of Data and Computational Science Duke Kunshan University Jiangsu 215316 China ZJU-Hangzhou Global Scientific and Technological Innovation Center School of Cyber Science and Technology Zhejiang University Zhejiang 310007 China

Speech recognition using EEG signals captured during covert (imagined) speech has garnered substantial interest in Brain–Computer Interface (BCI) research. While the concept holds promise, current implementations must improve performance compared to established Automatic Speech Recognition (ASR) methods using audio. An area often underestimated in previous studies is the potential of EEG utilization during overt speech. Integrating overt EEG signals with speech data by leveraging advancements in deep learning presents significant potential to enhance the efficacy of these systems. This integration proves particularly advantageous in noisy environments and for individuals with speech impairments—challenges even conventional ASR techniques struggle to address effectively. Our investigation delves into this relationship by introducing a novel multimodal model that merges EEG and speech inputs. Our model achieves a multiclass classification accuracy of 95.39%. When subjected to artificial white noise added to the input audio, our model exhibits a notable level of resilience, surpassing the capabilities of models reliant solely on single EEG or audio modalities. The validation process, leveraging the robust techniques of t-SNE and silhouette coefficient, corroborates and solidifies these advancements. © 2024 Elsevier Inc.

关键词： Automatic Speech Recognition (ASR) Brain–Computer Interface (BCI) Deep learning Electroencephalography (EEG) Multimodal analysis Multimodal speech recognition Neural signal processing Overt speech

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Spatial Relationship between Atrial Fibrillation Drivers and the Presence of Repetitive Conduction Patterns Using Recurrence Analysis on In-Silico Models

Spatial Relationship between Atrial Fibrillation Drivers and...

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2021 Computing in Cardiology, CinC 2021

作者： Marques, Victor G Gharaviri, Ali Pezzuto, Simone Bonizzi, Pietro Zeemering, Stef Schotten, Ulrich Maastricht University Physiology Department Maastricht Netherlands Università della Svizzera Italiana Center for Computational Medicine in Cardiology Euler Institute Lugano Switzerland Maastricht University Department of Data Science and Knowledge Engineering Maastricht Netherlands

ISBN: (纸本)9781665479165

Catheter ablation treatment for atrial fibrillation (AF) is still suboptimal, possibly due to the difficulty to identify AF drivers. Recurrence analysis can be used to detect and eventually locate repetitive patterns that tend to be generated by AF drivers. In this study, we aimed to understand the spatial relationship between repetitiveness in recurrence analysis and rotor positions in an in-silico AF model. AF was simulated in a detailed three-dimensional model of the atria considering different degrees of endomysial fibrosis (0% and 70%). Rotors driving AF were tracked based on phase singularities obtained from transmembrane potentials. Activation-phase signals calculated from electrograms (4×4 electrode grid, 3 mm spacing) were used for recurrence analysis. Intervals with and without long-lasting sources inside the electrode coverage area were determined;the recurrence in both groups of intervals was quantified and compared with each other by calculating the recurrence rate (RR) per AF cycle length. RRs were lower during intervals with sources for both 0% and 70% fibrosis groups (0.56 [0.36;0.85] vs. 0.90 [0.80;0.97], p © 2021 Creative Commons.

关键词： Diseases

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Hyper-spectral NIR and MIR data and optimal wavebands for detection of apple tree diseases

arXiv

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arXiv 2020年

作者： Shadrin, Dmitrii Pukalchik, Mariia Uryasheva, Anastasia Tsykunov, Evgeny Yashin, Grigoriy Rodichenko, Nikita Tsetserukou, Dzmitry Center for Computational and Data-Intensive Science and Engineering Moscow Russia Moscow Russia

Plant diseases can lead to dramatic losses in yield and quality of food, becoming a problem of high priority for farmers. Apple scab, moniliasis, and powdery mildew are the most significant apple tree diseases worldwide and may cause between 50% and 60% in yield losses annually;they are controlled by fungicide use with huge financial and time expenses. This research proposes a modern approach for analyzing the spectral data in Near-Infrared and Mid-Infrared ranges of the apple tree diseases at different stages. Using the obtained spectra, we found optimal spectral bands for detecting particular disease and discriminating it from other diseases and healthy trees. The proposed instrument will provide farmers with accurate, real-time information on different stages of apple tree diseases, enabling more effective timing, and selecting the fungicide application, resulting in better control and increasing yield. The obtained dataset as well as scripts in Matlab for processing data and finding optimal spectral bands are available via the link: https://***/d/ZqfGaNlYVR3TUA. Copyright © 2020, The Authors. All rights reserved.

关键词： Infrared devices

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Big data Fusion Challenge: Unmanned Aerial System Based Precision Agriculture

Big Data Fusion Challenge: Unmanned Aerial System Based Prec...

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2021 International Conference on Emerging Techniques in computational Intelligence, ICETCI 2021

作者： Gokaraju, Balakrishna Samiappan, Sathish Kale, Yogesh S. ViCAR Center and North Carolina AandT State University Computational Data Science and Engineering GreensboroNC27411 United States Mississippi State University Gosytems Research Institute StarkvilleMS39759 United States

ISBN: (纸本)9781665415590

The International Conference of computational Intelligence 2021 organized multi Remote Sensing data Competitions, and a Big data Fusion Challenge using UAS based precision Agriculture is coordinated by North Carolina A&T State University and Mississippi State University. The Big data Challenge focused on Multi-sensor and Multitemporal datasets including the hyper spectral imagery with 251 bands. A multi-class problem is given to classify three different crops and at three yield levels. The challenge received multiple submissions and the winning approach is based on inference learning using residual neural networks with 150 layers with a performance of Kappa accuracy as 0.86. © 2021 IEEE.

关键词： Remote sensing

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Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric KNbO3 perovskite

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Physical Review B 2023年第1期107卷 014101-014101页

作者： Hao-Cheng Thong XiaoYang Wang Jian Han Linfeng Zhang Bei Li Ke Wang Ben Xu State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing 100084 People's Republic of China Graduate School China Academy of Engineering Physics Beijing 100193 People's Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 People's Republic of China Beijing Institute of Big Data Research Beijing 100871 People's Republic of China School of Materials Science and Engineering Research Center for Materials Genome Engineering Wuhan University of Technology Wuhan 430070 People's Republic of China

Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which ecofriendly lead-free (K,Na)NbO3−based materials have been recently demonstrated to be an excellent candidate for sustainable development. Molecular dynamics is a versatile theoretical calculation approach for the investigation of the dynamical properties of ferroelectric perovskites. However, molecular dynamics simulation of ferroelectric perovskites has been limited to simple systems, since the conventional construction of interatomic potential is rather difficult and inefficient. In the present study, we construct a machine-learning interatomic potential of KNbO3 [as a representative system of (K,Na)NbO3] by using a deep neural network model. Including first-principles calculation data into the training data set ensures the quantum-mechanics accuracy of the interatomic potential. The molecular dynamics based on machine-learning interatomic potential shows good agreement with the first-principles calculations, which can accurately predict multiple fundamental properties, e.g., atomic force, energy, elastic properties, and phonon dispersion. In addition, the interatomic potential exhibits satisfactory performance in the simulation of domain wall and temperature-dependent phase transition. The construction of interatomic potential based on machine learning could potentially be transferred to other ferroelectric perovskites and consequently benefit the theoretical study of ferroelectrics.

关键词： Domain walls Elasticity Ferroelectricity First-principles calculations Phase transitions Phonons Piezoelectricity Machine learning Molecular dynamics

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Decoding Speech Categorization using Microstate Cortical EEG Signals and Machine Learning

Decoding Speech Categorization using Microstate Cortical EEG...

引用

IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： M. Mahmud M. Hasan M. Yeasin G. Bidelman Div. of General Internal Medicine Univ. of Tennessee Health Science Center Memphis TN USA Department of Electrical and Computer Engineering University of Memphis TN USA Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Department of Speech Language and Hearing Sciences Indiana University Bloomington IN USA

ISBN: (数字)9798350388572

ISBN: (纸本)9798350388589

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing.

关键词： Support vector machines Accuracy Hidden Markov models Phonetics Signal processing Electroencephalography Real-time systems Decoding Speech processing Random forests

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NC-ALG: Graph-Based Active Learning Under Noisy Crowd

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

引用

International Conference on data engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks Labeling Noise measurement

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A Novel data Augmentation Method for Robotic Surgical Instrument Small Part Segmentation in Complex Scenes 23

A Novel Data Augmentation Method for Robotic Surgical Instru...

引用

Proceedings of the 6th International Conference on Machine Learning and Machine Intelligence

作者： Dewei Yang Yongjian Li Xin Wang Jie Liu Xiaobo Zhou College of Advanced Manufacturing Engineering Chongqing University of Posts and Telecommunications China Biomedical Big Data Center West China Hospital of Sichuan University China Division of Pancreatic Surgery West China Hospital of Sichuan University China Chengdu Withai Innovations Technology Company China Center for Computational Systems Medicine University of Texas Health Science Center at Houston USA

ISBN: (纸本)9798400709456

Accurate and robust segmentation of surgical instrument parts is urgently required by AI enhanced intelligent surgery and automatic surgery skill evaluation. However, it is still a challenging problem due to the multiple small areas of frontal clasper and wrist parts, complicated object occlusion and light variation issues. Increasing high quality labeled data is effective to improve the performance but labor-costing. A novel instrument data augmentation method is proposed to generate high quality training data through planar instrument replacement and orientation adjustment. Moreover, image repainting methods are utilized to recover the occluded parts by instruments and generate clean tissue images. Then joint angle reconfigured instruments are placed on recovered clean tissue images to generate simulated surgery images, and we control the occlusion rates and overlapping areas by which high quality instrument manipulation augmented training data are generated automatically. Detailed experiment results show our proposed data augmentation method make existing methods achieve better segmentation accuracy and robustness of wrist and clasper part, especially the fault detection errors decreased significantly in instrument overlapping and complicated lighting conditions.

关键词： data augmentation Robotic instrument segmentation Small part segmentation

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