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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Xueyang Hu Tian Liu Minarul Islam Tao Shu Department of Broadband Communication Peng Cheng Laboratory China Institute of Agricultural Equipment Zhejiang Academy of Agricultural Sciences Hangzhou China Department of Computer Science and Software Engineering Auburn University USA

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

Estimation of wood moisture content (MC) is a fundamental aspect of woodworking, construction, and various other industries that rely on the versatile properties of wood. In this paper, we present mmMC, a novel system that estimates the MC of wood by a single commercial off-the-shelf (COTS) mmWave radar, which provides accurate end-to-end real-time wood moisture measurement results while being non-invasive, portable, and flexible in deployment. The proposed system uses a novel target reflection feature (TRF) to determine the reflectivity of the wood, thereby correlating it with specific MC levels. To robustly and accurately estimate the TRF of the wood from the mmWave signal, a signal processing pipeline is proposed. Specifically, an object detection and signal extraction module is designed to resolve the range of the object and eliminate multipath effects of the original signal. Then, a multiple chirps/antennas signals processing module is proposed to obtain a stable TRF from the extracted signals by using signals from different antennas and chirps. The TRF is associated with the MC of wood by a regression to enable real-time MC estimation. Through extensive real-world experiments, we have demonstrated that the proposed system has high accuracy and reliability.

关键词： Reflectivity Accuracy Radar measurements Moisture measurement Pipelines Moisture Reflection Real-time systems Sensors Millimeter wave communication

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A Spectrally Regulated Convolution-Based Network for Crop-Mapping with Hyperspectral Images

A Spectrally Regulated Convolution-Based Network for Crop-Ma...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Abhishek Singh Giulio Weikmann Lorenzo Bruzzone Department of Information Engineering and Computer Science Remote Sensing Laboratory (RSLab) University of Trento Trento Italy

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Addressing crop-mapping problems using high-resolution hyperspectral images requires innovative solutions that integrate advanced image processing and machine learning. In this paper, we propose an approach designed to address the main challenges of crop mapping using hyperspectral images, including the analysis of high-dimensional feature spaces, the limited number of training samples and the complex spectral-spatial relationships. The proposed architecture exploits Contractive-Expansive-Contractive (CEC) connections, which extract features capable of capturing both global and local patterns, while enhance discriminative features and suppress noise and irrelevant information. The CEC network is used to integrate multiscale features and learn the optimal decision boundaries for segmentation. The proposed architecture is evaluated on a Prisma hyperspectral dataset and compared against other methodologies suited for crop-type mapping. The experimental results demonstrate that the proposed approach achieves higher overall and class-wise segmentation accuracy.

关键词： Training Image segmentation Noise Crops Machine learning Feature extraction Market research

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization 12

Cyber-Security Knowledge Graph Generation by Hierarchical No...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Wanna, Selma Solovyev, Nicholas Rasmussen, Kim Alexandrov, Boian S. Nicholas, Charles Matuszek, Cynthia Los Alamos National Laboratory Theoretical Division Los Alamos United States Los Alamos National Laboratory Advanced Research in Cyber Systems Los Alamos United States University of Maryland Department of Computer Science and Electrical Engineering Baltimore CountyMD United States

ISBN: (纸本)9798350330366

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG. © 2024 IEEE.

关键词： Cybersecurity

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781957171395

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Optical fibers

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Estimating ECG Intervals from Lead-I Alone: External Validation of Supervised Models

arXiv

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arXiv 2024年

作者： Alam, Ridwan Stultz, Collin M. the Research Laboratory of Electronics the Department of Electrical Engineering Computer Science the Massachusetts Institute of Technology United States

The diagnosis, prognosis, and treatment of a number of cardiovascular disorders rely on ECG interval measurements, including the PR, QRS, and QT intervals. These quantities are measured from the 12-lead ECG, either manually or using automated algorithms, which are readily available in clinical settings. A number of wearable devices, however, can acquire the lead-I ECG in an outpatient setting, thereby raising the potential for out-of-hospital monitoring for disorders that involve clinically significant changes in ECG intervals. In this work, we therefore developed a series of deep learning models for estimating the PR, QRS, and QT intervals using lead-I ECG. From a corpus of 4.2 million ECGs from patients at the Massachusetts General Hospital, we train and validate each of the models. At internal holdout validation, we achieve mean absolute errors (MAE) of 6.3 ms for QRS durations and 11.9 ms for QT intervals, and a MAE of 9.2 ms for estimating PR intervals. Moreover, as a well-defined P-wave does not always exist in ECG tracings – for example, when there is atrial fibrillation – we trained a model that can identify when there is a P-wave, and consequently, a measurable PR interval. We validate our models on three large external healthcare datasets without any finetuning or retraining - 3.2 million ECG from the Brigham and Women’s Hospital, 668 thousand from MIMIC-IV, and 20 thousand from PTB-XL - and achieve similar performance. Also, our models significantly outperform two publicly available baseline algorithms. This work demonstrates that ECG intervals can be tracked from only lead-I ECG using deep learning, and highlights the potential for out-of-hospital applications. © 2024, CC BY-NC-ND.

关键词： Electrocardiography

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A study on the impact of pre-trained model on Just-In-Time defect prediction

A study on the impact of pre-trained model on Just-In-Time d...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Yuxiang Guo Xiaopeng Gao Zhenyu Zhang W.K. Chan Bo Jiang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Computer Science City University of Hong Kong Hong Kong China

Previous researchers conducting Just-In-Time (JIT) defect prediction tasks have primarily focused on the performance of individual pre-trained models, without exploring the relationship between different pre-trained models as backbones. In this study, we build six models: RoBERTaJIT, CodeBERTJIT, BARTJIT, PLBARTJIT, GPT2JIT, and CodeGPTJIT, each with a distinct pre-trained model as its backbone. We systematically explore the differences and connections between these models. Specifically, we investigate the performance of the models when using Commit code and Commit message as inputs, as well as the relationship between training efficiency and model distribution among these six models. Additionally, we conduct an ablation experiment to explore the sensitivity of each model to inputs. Furthermore, we investigate how the models perform in zero-shot and few-shot scenarios. Our findings indicate that each model based on different backbones shows improvements, and when the backbone’s pre-training model is similar, the training resources that need to be consumed are closer. We also observe that Commit code plays a significant role in defect detection, and different pre-trained models demonstrate better defect detection ability with a balanced dataset under few-shot scenarios. These results provide new insights for optimizing JIT defect prediction tasks using pre-trained models and highlight the factors that require more attention when constructing such models. Additionally, CodeGPTJIT and GPT2JIT achieved better performance than DeepJIT and CC2Vec on the two datasets respectively under 2000 training samples. These findings emphasize the effectiveness of transformer-based pre-trained models in JIT defect prediction tasks, especially in scenarios with limited training data.

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Full-Wave Simulations Using Fast Hybrid Method for L-Band Microwave Propagation in Forested Areas

Full-Wave Simulations Using Fast Hybrid Method for L-Band Mi...

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Antennas and Propagation & USNC/URSI National Radio science Meeting

作者： Jongwoo Jeong Leung Tsang Zhenming Huang Haokui Xu Andreas Colliander Department of Electrical Engineering and Computer Science University of Michigan Jet Propulsion Laboratory California Institute of Technology

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

We perform full-wave simulations of propagation of L-band microwave signals in forested areas to study forest effects on microwave remote sensing, wireless communications, and hidden object detection. Initially, full-wave simulations of forests were limited to eight-meter-tall trees. With the aid of computational enhancement by a fast hybrid method (FHM) solving Maxwell's equations, a height of trees extends to 13 m followed by 17 m. The FHM applies to 91 trees of a height of 17 m, where the volumetric space in the simulation domain is equivalent to 133 wavelengths by 133 wavelengths and 80 wavelengths. We will continue to investigate the effects of tapering and bifurcated trunks, as well as the presence of leaves, on microwave remote sensing. We illustrate the spatial distribution of electric fields under forests. Computational efficiency of the FHM is also discussed. The results indicate that L-band microwave signals can penetrate through forests, enabling the detection of soil and objects below, and GPS signals can be successfully received by receivers beneath forested areas.

关键词： Computational modeling Forestry Vegetation Microwave communication Soil Microwave theory and techniques Mathematical models

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Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

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A GUI Based Approach to Detect Energy Bugs in Android Applications 16

A GUI Based Approach to Detect Energy Bugs in Android Applic...

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16th International Conference on Emerging Technologies, ICET 2021

作者： Naseer, Ayesha Nadeem, Aamer Zaman, Qamar Uz Capital Universoty of Science Technology Department of Computer Science Islamabad Pakistan Capital Universoty of Science Technology Software Engineering Islamabad Pakistan Capital Universoty of Science Technology Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665494373

Smartphones have improved in terms of their processing speed and memory capacity. The increased usage of smartphones has led to the widespread availability of smartphone applications. However, many smartphone applications suffer from energy inefficiency problems due to improper usage of resources. When resources are left unreleased after use, they keep consuming smartphone's battery power even if the application is not using these resources. This behavior is called resource-leak which causes energy-bugs. Different solutions based on static, dynamic and hybrid analysis exist in the literature to detect energy-bugs due to resource-leaks. However, most of the existing solutions suffer from the problem of false positives due to energy-bugs detection at the method level. They consider each method in isolation and check that if the particular method has acquired a resource but has not released it within that method. In practice, it is common that resources are acquired in one method and released in another method. In this paper, we propose a dynamic analysis technique to perform energy-bug detection at the application level to overcome the problem of false-positives. Our aim is to detect energy bugs, because serious energy issues can arise in smartphones due to energy bugs. Besides, we have considered methods in which resource is acquired or released, and if both methods execute in a single path, then the resource will be released and there will be no energy bug at application level. To evaluate our proposed approach, we have performed experiments on 11 real-life Android applications. Experimental results show that our approach is able to identify real energy bugs at the application level as well as identify false positives. © 2021 IEEE.

关键词： Smartphones

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DGPDHGCN: A Heterogeneous Graph Convolutional Network Method for Predicting Drug-Disease Associations

DGPDHGCN: A Heterogeneous Graph Convolutional Network Method...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Haoran Yu, Tong Ge, Ling Wang, Jianjia Xi'an Jiaotong-Liverpool University School of Ai and Advanced Computing Xjtlu Entrepreneur College Suzhou China Shanghai University School of Computer Engineering and Science China Huaibei People's Hospital Department of Clinical Laboratory Huaibei China

ISBN: (纸本)9798350386226

Drug repositioning is a crucial aspect of biomedical research, and predicting drug-disease associations (DDAs) is a critical step in this process. With the development of deep learning and neural network technologies, Graph Convolutional Networks (GCNs) have achieved significant performances in this research field. Although existing DDAs models have made substantial progress, there is still need for improvement in sufficiently utilizing and integrating information from multiple biological entities. In this study, we propose a Drug-Gene-Protein-Disease Heterogeneous Graph Convolutional Network (DGPDHGCN) model for drug-disease association prediction. First, we construct a heterogeneous network from multiple data sources and establish meta-paths based on the topological information of biological entities. Then, the DGPDHGCN model learns representations of drugs and diseases from similarity and association data of those entities. Finally, we define a score function to quantify the associations between drugs and diseases. Through extensive experiments, we demonstrate that DGPDHGCN outperforms baseline models in DDAs prediction tasks in terms of metrics such as AUPR, F1-score, precision, and recall. The source code and experimental datasets can be found in https://***/Saxon0918/DGPDHGCN © 2024 IEEE.

关键词： Convolutional neural networks

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