Reviewerly/exHarmony huggingface dataset:
Super-sensitivity full-field measurement of structural vibration with an adaptive incoherent optical method

Full-field measurements of structural vibrations have been achieved by incoherent optical methods with video cameras such as digital image correlation and optical flow. In contrast to coherent optical methods such as scanning laser vibrometers, incoherent optical methods are low-cost and easy to set up. However, the sensitivity (minimum measurable displacement) of incoherent optical methods is generally lower than that of coherent optical methods. Typically, the sensitivity of incoherent optical methods is essentially limited by the finite bit depth of the digital camera due to the quantization with round-off errors. Quantitatively, this theoretical sensitivity limit is determined by the bit depth B as δp=1/(2B−1) [pixel] which corresponds to a displacement causing an intensity change of one gray level. Fortunately, natural dithering may be leveraged to overcome the quantization and exceed the sensitivity limit, achieving super-sensitivity. In this work, we first study the mechanism of the sensitivity limit induced by the quantization and the critical limitation of existing super-sensitivity methods in the full-field measurement of structural vibration. Addressing such a limitation, we present a general adaptive weighted averaging method with dithering, achieving super-sensitivity incoherent optical measurement of full-field deformational vibration of flexible structures. Specifically, both spatial and temporal samplings of the pixels are leveraged simultaneously to adaptively identify the motion (deformation) shape of the structure by principal component analysis (PCA) of spatiotemporal pixel intensities. Then, the identified deformation shape is used to perform a deformation-shape-weighted averaging over spatial pixels with dithering, revealing the sub-sensitivity displacement while retaining spatial resolution at full field. Numerical simulations and laboratory experiments are performed for principle explanation and method validation. Particularly, we derive a mathematical model of the minimum measurable vibration amplitude A∗ for the developed super-sensitivity method, which is found to be determined by the number of spatial pixels for averaging Ns and the noise level σn in the imaging system. Thus, this work provides, for the first time, a rigorous quantification of the sensitivity of incoherent optical measurement for structural vibration, and a new quantitative method to achieve super-sensitivity full-field measurement of structural vibration.
Feature-wise attention based boosting ensemble method for fraud detection

Transaction fraud detection is an essential topic in financial research, protecting customers and financial institutions from suffering significant financial losses. The existing ensemble learning-based fraud detection model improves the accuracy of distinguishing fraudulent transactions by integrating multiple base learners. However, their insufficient optimization of variance leads to the inadequate generalization of the model, which makes them unable to meet the requirements of detecting evolving fraudulent behavior. So, this paper proposes an attention-based boosting ensemble model (AM-Boost) for fraud detection that imparts diversity perturbation to the base learner from the feature dimension. Firstly, the feature-wise attention mechanism is proposed to help the model exploit the highly informative fraud features by learning distributed global feature space. And it is combined with boosting strategy to reduce the variance by lowering the correlation between base learners. Secondly, we propose a feature diversity regularization term in the integration stage to optimize the weighted majority voting strategy with a reasonable trade-off between bias and variance. Finally, we evaluate the proposed method on a public fraud detection dataset and a real-world transaction dataset. Extensive experiment results demonstrate that our method outperforms the baseline methods in both datasets.
Instance segmentation of stack composed of unknown objects

The article reviews neural network architectures designed for the segmentation task. It focuses mainly on instance segmentation of stacked objects. The main assumption is that segmentation is based on a color image with an additional depth layer. The paper also introduces the Stacked Bricks Dataset based on three cameras: RealSense L515, ZED2, and a synthetic one. Selected architectures: DeepLab, Mask RCNN, DEtection TRansformer, Geometry-Aware Instance Segmentation, Shapemask, Synthetic Depth Mask RCNN, Synthetic Fusion Mask RCNN (SF-Mask), Unseen Object Instance Segmentation (UOIS), Unseen Object Clustering (UOC), and You Look Only At Coefficients, have been tested on various datasets. The results show that the best architectures for stacked elements segmentation are UOIS, SF-Mask, and UOC.
Nonparametric functional analysis under joint estimation with applications to identifying highly cited papers

This article introduces a nonparametric methodology combining the strengths of binary regression and latent variable formulations, while overcoming their disadvantages. The mathematical results are implemented through a novel Bayesian Hierarchical estimation methodology called Latent Adaptive Hierarchical Expectation Maximization Like algorithm. Requiring minimal assumptions, it extends extant methodologies, and in simulation studies gives better prediction and inference performances for asymmetric data generating processes. A new classification statistic, called Adjusted Receiver Operating Curve Statistic is also introduced. Utilizing it we demonstrate better overall model fit, inference and prediction performance of the proposed methodology over widely used existing methods in the sciences. In addition, the methodology can be used to perform model diagnostics for any model specification. This is a highly useful result, and it extends existing work for categorical model diagnostics broadly across the sciences. Furthermore, the mathematical results also highlight important new findings regarding the interplay of statistical significance and scientific significance. Finally, the methodology is applied to identifying highly-cited papers in the social sciences in a joint estimation framework. The results indicate that the methodology outperforms widely used existing artificial intelligence and machine learning models with very few Monte Carlo iterations. In Scientometric application, it finds Journal Impact Factor to be more important than Keyword Popularity parameters for explaining citation outcomes in select social science fields. It further finds that the percentage change in Published Popularity may also help to explain citation outcomes in the field. The findings appear to be new to the Scientometric field.
Uniform instruction set extensions for multiplications in contemporary and post-quantum cryptography

Abstract Hybrid key encapsulation is in the process of becoming the de-facto standard for integration of post-quantum cryptography (PQC). Supporting two cryptographic primitives is a challenging task for constrained embedded systems. Both contemporary cryptography based on elliptic curves or RSA and PQC based on lattices require costly multiplications. Recent works have shown how to implement lattice-based cryptography on big-integer coprocessors. We propose a novel hardware design that natively supports the multiplication of polynomials and big integers, integrate it into a RISC-V core, and extend the RISC-V ISA accordingly. We provide an implementation of Saber and X25519 to demonstrate that both lattice- and elliptic-curve-based cryptography benefits from our extension. Our implementation requires only intermediate logic overhead, while significantly outperforming optimized ARM Cortex M4 implementations, other hardware/software codesigns, and designs that rely on contemporary accelerators.
A novel Federated Computation approach for Artificial Intelligence applications in Delay and Disruption Tolerant Networks

Communication between the Earth and distant spacecraft is challenging, since there are frequent link outages due to orbital trajectories, loss of signal due to noise or antenna pointing problems, and long and highly variable, one-way light time delays due to the extreme orbits and distances involved. Deep space link reliability has traditionally required manual intervention by Mission Operations teams, and new Solar System Internetworking protocols known as Delay and Disruption-Tolerant Networking (DTN) have been developed to automate space communications. This automation now provides a mechanism for space networking and link performance optimization using artificial intelligence (AI) and machine learning (ML) methods which we present in this paper. We discuss Federated Learning (FL) and our application of FL to monitoring and optimization of DTN networks. Federated learning is a distributed learning framework that enables machine learning to be performed on decentralized data with improved protection for each collaborator’s data privacy and communication efficiency. This work outlines a novel approach to advancing current AI applications running over delay and disruption tolerant networks by leveraging two forms of federated learning for space applications, and the core DTN Bundle Protocol to produce smarter ‘on-edge device’ machine learning models. Our work demonstrates that the network can operate with more link bandwidth efficiency and less resource consumption, while ensuring privacy and quality constraints. Experiments on a popular image dataset show that DTN-ML can have similar or better performance when compared to existing alternatives, but with much less communication overhead.
Occluded and Deformed Jersey Numbers Recognition by Hourglass Networks with Deformable Convolutional Networks

We address the problem of basketball jersey number recognition, where the jersey numbers may be partially occluded, deformed, or bent. These challenges make it difficult to accurately recognize the jersey numbers, especially in real-world scenarios with varying player postures, angles, lighting conditions, jersey colors, and patterns. In this paper, we propose a novel method for basketball jersey number recognition, name deformable hourglass network (DHN), by integrating deformable convolutional network v3 (DCNv3) into the hourglass architecture, which is inspired by the convolutional character networks (CharNet) model. We also provide a new dataset, which contains various occluded jersey numbers in real basketball contest scenarios. We show that the DHN can achieve better recognition accuracy and robustness of the deformed basketball jersey number in challenging real-world scenarios.
Similarity-Based Shield Adaptation under Dynamic Environment

In this paper, we present an efficient design of a shield called Parameterized Shield which allows for easy modification of its requirement parameters in response to the changes in the environment instead of manually making many shields during the development time and changing the whole shield during runtime. We propose to generate one proper shield that has many requirement parameters where even when there is any complex situation, there is not any need for extra effort from engineers to develop additional requirements and prepare the shield but only the parameters need to be changed when there is a change in the environment which avoids choosing a whole new shield every time an environment change occurs during runtime. And similarity-based adaptation will be used to choose the right parameters for the shield according to the environment with the help of the MAPE-K loop framework and reinforcement learning and to give the appropriate policy for the agent to act according to that environment. We will demonstrate using a cleaning robot as an example.
Test-Time Adaptation in the Dynamic World With Compound Domain Knowledge Management

Prior to the deployment of robotic systems, pre-training the deep-recognition models on all potential visual cases is infeasible in practice. Hence, test-time adaptation (TTA) allows the model to adapt itself to novel environments and improve its performance during test time (i.e., lifelong adaptation). Several works for TTA have shown promising adaptation performances in continuously changing environments. However, our investigation reveals that existing methods are vulnerable to dynamic distributional changes and often lead to overfitting of TTA models. To address this problem, this paper first presents a robust TTA framework with compound domain knowledge management. Our framework helps the TTA model to harvest the knowledge of multiple representative domains ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , compound domain) and conduct the TTA based on the compound domain knowledge. In addition, to prevent overfitting of the TTA model, we devise novel regularization which modulates the adaptation rates using domain-similarity between the source and the current target domain. With the synergy of the proposed framework and regularization, we achieve consistent performance improvements in diverse TTA scenarios, especially on dynamic domain shifts. We demonstrate the generality of proposals via extensive experiments including image classification on ImageNet-C and semantic segmentation on GTA5, C-driving, and Cityscapes datasets.
SPECIL: Spell Error Corpus for the Indonesian Language

In this study, we present the first spell error corpus for the Indonesian Language (SPECIL). This corpus provides a comprehensive resource for researchers and practitioners to detect and correct spelling errors in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bahasa Indonesia</i> (Indonesian). It should be emphasized that currently, there is no recognized corpus for identifying spelling mistakes in the Indonesian language that has been officially released or made accessible. This study also provides a systematic literature review to identify resources and methodologies for building a corpus for spelling error detection and correction in Indonesia. A corpus was created using a combination of manual and automatic methods. The results of this study are a review of publications relating to corpora and spelling, the novel algorithm of six types of spelling errors, and the production of a corpus comprising over 180,000 tokens in 21,500 sentences, including non-word, real-word, and punctuation errors. Using the developed corpus, various Natural Language Processing (NLP) models, including spell checkers and language models, can be trained and tested to enhance their accuracy and effectiveness in identifying and rectifying errors in Indonesian texts. Moreover, the corpus can be used to develop and evaluate new algorithms and techniques for spelling error detection and correction in Indonesia. The SPECIL corpus is publicly available and accessible. It is expected that SPECIL will inspire further research in this area and facilitate the development of more accurate and effective spelling error detection and correction tools in Indonesian language.
DA-FSOD: A Novel Data Augmentation Scheme for Few-Shot Object Detection

Deep learning techniques continue to be used in various applications in recent years. However, when it is difficult to obtain adequate training samples, the performance of the depth model will degrade. Although few-shot learning and data enhancement techniques can relieve this dilemma, the diversity of real data is too large to simulate. To tackle this challenge, we study a novel method, Data Augmentation Scheme For Few-Shot Object Detection (DA-FSOD), to improve the efficiency of model training on visual tasks. Specifically, to expand data augmentation space, we build a data augmentation operation pool (DAOP) based on several common-applied image process operations. Then we propose a novel data augmentation scheme, the series and parallel connection scheme, which superimposes the effects of different operations to generate diverse variants. To further explore and utilize the deep feature information, we leverage the semantic information of input image in model and propose imposed semantic data augmentation which augments training set semantically via deep features of augmented variants. The proposed method successfully enhanced the model performance. We validated our approach using extensive experiments on the domain of few-shot object detection. The results showed remarkable gains compared to state-of-the-art methods.
Unified Network With Detail Guidance for Panoptic Segmentation

Panoptic segmentation has won popularity in image perception for its unique advantages. A generic backbone is utilized to extract image features, either fusing semantic and instance segmentation results or end-to-end. Backbone is able to merge low-level details and high-level semantics. However, in practice, detailed information is weakened after deep convolutions. To address this limitation, we propose a novel unified network consisting of a bilateral feature extraction structure and an aggregation module. Both detail and semantic information extraction are decoupled successfully. Specifically, the bilateral feature extraction structure comprises two main branches. One branch uses a generic backbone to obtain the rich receptive field, while the other uses the guidance of detail ground-truth to extract low-level features. Furthermore, the aggregation module combines the results of two branches to obtain a large receptive field with detailed information. Comparative experiments are performed on COCO and Cityscapes datasets. The results demonstrate that high accuracy is obtained. Among them, 41.3 panoptic quality is achieved on COCO, and 59.9 is achieved on Cityscapes.
Directional Alignment Instance Knowledge Distillation for Arbitrary-Oriented Object Detection

Recently, many lightweight neural networks have been deployed on airborne or satellite remote sensing platforms for real-time object detection. To bridge the performance gap between lightweight models and complex models, many knowledge distillation (KD) methods are investigated. However, existing KD methods ignore to transfer effective directional knowledge. Meanwhile, knowledge of different subtasks interfere with each other. To this end, a directional alignment instance knowledge distillation (DAIK) method for improving the performance of the lightweight object detection model is proposed. Specifically, an angle distillation (AD) module is developed to combine the circular smooth label and teacher logits to transfer effective directional knowledge. Angular-distance Aspect-ratio Look-up-table (AAL) is incorporated into label assignment and re-weighting loss to enhance the prediction sensitivity of direction and shape in a discrete manner. Sample alignment distillation (SAD) reduces the spatial misalignment by mimicking the teacher model’s distribution of anchor points. Extensive experiments are performed on several public remote sensing object detection datasets, which demonstrates the effectiveness of the proposed DAIK.
Continual Nuclei Segmentation via Prototype-Wise Relation Distillation and Contrastive Learning

Deep learning models have achieved remarkable success in multi-type nuclei segmentation. These models are mostly trained at once with the full annotation of all types of nuclei available, while lack the ability of continually learning new classes due to the problem of catastrophic forgetting. In this paper, we study the practical and important class-incremental continual learning problem, where the model is incrementally updated to new classes without accessing to previous data. We propose a novel continual nuclei segmentation method, to avoid forgetting knowledge of old classes and facilitate the learning of new classes, by achieving feature-level knowledge distillation with prototype-wise relation distillation and contrastive learning. Concretely, prototype-wise relation distillation imposes constraints on the inter-class relation similarity, encouraging the encoder to extract similar class distribution for old classes in the feature space. Prototype-wise contrastive learning with a hard sampling strategy enhances the intra-class compactness and inter-class separability of features, improving the performance on both old and new classes. Experiments on two multi-type nuclei segmentation benchmarks, i.e., MoNuSAC and CoNSeP, demonstrate the effectiveness of our method with superior performance over many competitive methods. Codes are available at https://github.com/zzw-szu/CoNuSeg.
Methods and application of AI microseismic monitoring based on sparse stations

PreviousNext No AccessSEG Integration of Geophysics, Geology, and Engineering Workshop, Chengdu, China, 26–28 June 2023Methods and application of AI microseismic monitoring based on sparse stationsAuthors: Li ZizhengZhu HaiboWang YuSun ZhentaoLin ZhengliangLi ZizhengSinopec Petroleum Geophysical Technology Research Institute Co., Ltd.Search for more papers by this author, Zhu HaiboSinopec Petroleum Geophysical Technology Research Institute Co., Ltd.Search for more papers by this author, Wang YuSinopec Petroleum Geophysical Technology Research Institute Co., Ltd.Search for more papers by this author, Sun ZhentaoSinopec Petroleum Geophysical Technology Research Institute Co., Ltd.Search for more papers by this author, and Lin ZhengliangSinopec Petroleum Geophysical Technology Research Institute Co., Ltd.Search for more papers by this authorhttps://doi.org/10.1190/igge2023-23.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit Abstract In recent years, the market of microseismic monitoring is gradually shifting to long-term dynamic monitoring such as oil and gas field development monitoring and underground gas storage, and the technology of microseismic need to be upgraded for those new applications. The sparse stations in shallow wells have low cost and flexible layout, and gradually become a new microseismic data acquisition method. Some instruments (such as 5G seismic stations) can also send seismic data to the cloud for real time processing. In recent years, the AI processing algorithm of microseimic data (seismic phase picking, denoising, relocation) develop rapidly. Take seismic picking as an example, after transfer learning for specific areas, the accuracy of seismic phase picking can outperform human analysts so the automatic picking procedure can be established. This report mainly introduces the research on microseismic signal event identification, location and other processing methods as well as actual data testing carried out by the SGRI in response to the application of long-term dynamic monitoring during the development and production stage of large oil and gas field platforms. Keywords: monitoring, microseismic, gasPermalink: https://doi.org/10.1190/igge2023-23.1FiguresReferencesRelatedDetails SEG Integration of Geophysics, Geology, and Engineering Workshop, Chengdu, China, 26–28 June 2023ISSN (online):2159-6832Copyright: 2023 Pages: 142 publication data© 2023 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 22 Aug 2023 CITATION INFORMATION Li Zizheng, Zhu Haibo, Wang Yu, Sun Zhentao, and Lin Zhengliang, (2023), "Methods and application of AI microseismic monitoring based on sparse stations," SEG Global Meeting Abstracts : 96-98. https://doi.org/10.1190/igge2023-23.1 Plain-Language Summary KeywordsmonitoringmicroseismicgasPDF DownloadLoading ...