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arXiv 2023年

作者： Quan, Chen Geng, Baocheng Han, Yunghsiang S. Varshney, Pramod K. Department of Electrical Engineering and Computer Science Syracuse University United States Department of Computer Science University of Alabama at Birmingham United States Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

This paper proposes a belief-updating scheme in a human-machine collaborative decision-making network to combat Byzantine attacks. A hierarchical framework is used to realize the network where local decisions from physical sensors act as reference decisions to improve the quality of human sensor decisions. During the decision-making process, the belief that each physical sensor is malicious is updated. The case when humans have side information available is investigated, and its impact is analyzed. Simulation results substantiate that the proposed scheme can significantly improve the quality of human sensor decisions, even when most physical sensors are malicious. Moreover, the performance of the proposed method does not necessarily depend on the knowledge of the actual fraction of malicious physical sensors. Consequently, the proposed scheme can effectively defend against Byzantine attacks and improve the quality of human sensors' decisions so that the performance of the human-machine collaborative system is enhanced. © 2023, CC BY-NC-SA.

关键词： Decision making

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Automated Phenotyping of Herbaceous Biomass Using U-Net Architecture for μ-CT Images Segmentation

Automated Phenotyping of Herbaceous Biomass Using U-Net Arch...

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Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Antora Dev Yomna Mohamed Ahmed Hamed Yidong Xia Robert Seifert Mostafa M. Fouda Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Idaho National Laboratory Idaho Falls ID USA

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

Phenotyping is an essential process to characterize biomass feedstocks and assess the critical traits for efficient biofuel production. In this regard, micro-computed tomography (μ-CT) scanning is a powerful 3D imaging technique to analyze the microstructural features of the feedstock under consideration quantitatively. For this purpose, accurate segmentation of the acquired μ-CT images is crucial for reliable prediction of the biomass feedstock attributes. The present study employs a U-Net architecture for automated, high throughput segmentation of corn stalks μ-CT scanning images. advanced image preprocessing techniques, including adaptive thresholding and morphological operations, were integrated into the workflow to enhance the clarity and quality of the segmentation outputs. Comprehensive performance metrics to evaluate the accuracy of the segmented images in terms of the identified vascular bundles are also presented. The performance of the U-Net model was validated through pixel-by-pixel matching accuracy, overlap percentage of predicted masks with manual and Photoshop-generated masks, and robustness analysis of unseen data. The results demonstrate high segmentation precision, with the model achieving a test accuracy of 97.98% and significant overlap with reference masks, indicating its applicability in real-world scenarios for agricultural research and precision farming.

关键词： Image segmentation Adaptation models Accuracy Thresholding (Imaging) Biological system modeling Manuals Predictive models Biomass Morphological operations Farming

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Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data

Traffic Smoothing Controllers for Autonomous Vehicles Using ...

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International Conference on Intelligent Transportation

作者： Nathan Lichtlé Kathy Jang Adit Shah Eugene Vinitsky Jonathan W. Lee Alexandre M. Bayen Department of Electrical Engineering and Computer Sciences UC Berkeley CERMICS École des Ponts ParisTech Department of Mechanical Engineering UC Berkeley Institute for Transportation Studies UC Berkeley

Designing traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing fuel efficiency in mixed autonomy traffic. We bypass the common issue of having to carefully fine-tune a large traffic micro-simulator by leveraging real-world trajectory data from the I–24 highway in Tennessee, replayed in a one-lane simulation. Using standard deep reinforcement learning methods, we train energy-reducing wave-smoothing policies. As an input to the agent, we observe the speed and distance of only the vehicle in front, which are local states readily available on most recent vehicles, as well as non-local observations about the downstream state of the traffic. We show that at a low 4% autonomous vehicle penetration rate, we achieve significant fuel savings of over 15% on trajectories exhibiting many stop-and-go waves. Finally, we analyze the smoothing effect of the controllers and demonstrate robustness to adding lane-changing into the simulation as well as the removal of downstream information.

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Intriguing Effect of the Correlation Prior on ICD-9 Code Assignment 61

Intriguing Effect of the Correlation Prior on ICD-9 Code Ass...

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61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Yang, Zihao Zhang, Chenkang Wu, Muru Liu, Xujin Chris Jiang, Lavender Yao Cho, Kyunghyun Oermann, Eric Karl Center for Data Science New York University United States Department of Neurosurgery NYU Langone Health United States Department of Electrical and Computer Engineering NYU Tandon School of Engineering United States Courant Institute of Mathematical Sciences New York University United States Canadian Institute for Advanced Research Canada Prescient Design United States Department of Radiology NYU Langone Health United States Neuroscience Institute NYU Langone Health United States

ISBN: (纸本)9781959429692

The Ninth Revision of the International Classification of Diseases (ICD-9) is a standardized coding system used to classify health conditions. It is used for billing, tracking individual patient conditions, and for epidemiology. The highly detailed and technical nature of the codes and their associated medical conditions make it difficult for humans to accurately record them. Researchers have explored the use of neural networks, particularly language models, for automated ICD-9 code assignment. However, the imbalanced distribution of ICD-9 codes leads to poor performance. One solution is to use domain knowledge to incorporate a useful prior. This paper evaluates the usefulness of the correlation bias: we hypothesize that correlations between ICD-9 codes and other medical codes could help improve language models' performance. We showed that while the correlation bias worsens the overall performance, the effect on individual class can be negative or positive. Performance on classes that are more imbalanced and less correlated with other codes is more sensitive to incorporating the correlation bias. This suggests that while the correlation bias has potential to improve ICD-9 code assignment in certain cases, the applicability criteria need to be more carefully studied. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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MIMO Beamforming and Signal Modulation Design for Federated Learning Optimization

MIMO Beamforming and Signal Modulation Design for Federated ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Cong Shen Changchuan Yin Christopher G. Brinton Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, we consider the optimization of federated learning (FL) over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp). In such a system, MIMO devices transmit their locally trained FL models to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. AirComp enables efficient wireless model aggregation by the PS in bandwidth-limited settings. However, wireless channel fading can produce distortions in AirComp-based FL. To tackle this challenge, we develop a novel aggregation scheme that combines digital modulation with AirComp to mitigate wireless fading while ensuring communication efficiency. We formulate this as a joint transmit-receive beamforming design optimization problem which dynamically adjusts the beamforming matrices to minimize the FL training loss with transmission errors. To solve this problem based on limited information at the PS, we employ an artificial neural network (ANN) to estimate the local FL models of all devices. Then, we derive a closed-form optimal design of the transmit and receive beamforming matrices based on predicted FL models. Numerical evaluations validate the advantages of the proposed methodology in terms of model training performance compared with baselines.

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Future Trends and Short-Review on Fish Species Classification Models Based on Deep Learning Approaches

Future Trends and Short-Review on Fish Species Classificatio...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： M. Bhanumathi B. Arthi Department of Computer Science and Engineering SRM Institute of Science and Technology Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies(VISTAS) Department of Computer Science and Engineering Faculty of Engineering and Technology College of Engineering and Technology SRM Institute of Science and Technology

ISBN: (纸本)9781665489638

An automatic fish species classification system is highly essential to track, classify and detect marine species and fishes in complex underwater backgrounds without any manual help. Existing computer vision-based approaches didn't offer effective performance rates underwater because the textural features and shape of fish species are not apparent. The data-driven classification approach needsa superior amount of labeled data;otherwise, they leadtooverfitting at the time of data training, and also, the unseen test data are not utilized for ***, researchers, taxonomists, and geneticists from different biological fields wished to accept fish as a significant element in their research. Theywere discouraged from finding ichthyology,a highly complex task. In fish-based studies, failing to recognize fishes as distinct biological units can lead to thewrong diagnosis. Hence, this survey paper tries to discuss and clarify the recent research on species identification with their algorithmic categorization. This review explores the performance measures compared, the number of fish families/species recognized, datasets used, and tools utilized for implementation. Further, future research directions and compensation for current research gaps are discussed. This review of state-of-the-art fish identification tools shows their potential for providing the right solution in real-life situations.

关键词： Deep learning Training Machine learning algorithms Shape Current measurement Manuals Fish

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Analysis of Improving Sales Process Efficiency with Salesforce Industries CPQ in CRM 7th

Analysis of Improving Sales Process Efficiency with Salesfor...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Pathak, Pritesh Pal, Souvik Maity, Saikat Jeyalaksshmi, S. Adhikari, Saurabh Akila, D. Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Management Information Systems Saveetha College of Liberal Arts And Sciences Saveetha Institute of Medical And Technical Sciences Chennai India Kolkata India Department of Information Technology Vels Institute of Science Technology and Advanced Studies Chennai India School of Engineering Swami Vivekananda University Kolkata India Department of Computer Applications Saveetha College of Liberal Arts And Sciences Saveetha Institute of Medical And Technical Sciences Chennai India

ISBN: (纸本)9789819995615

The successful execution of Configure, Price, Quote (CPQ) protocols is of utmost importance for businesses that handle intricate product portfolios within their sales processes. The objective of these approaches is to optimise the generation of competitive quotations by leveraging data from diverse business systems, leading to a decrease in processing time and enhancement of operational efficiency. Salesforce's Industries CPQ for Communications Cloud, previously referred to as Vlocity CPQ, assumes a crucial function within the Salesforce platform by facilitating the automation of the quotation creation process for sales teams. Designed specifically for the communications industry, this solution enables communications service providers (CSPs) to effectively provide a wide array of products and services to their customers in a streamlined manner. By implementing Industries CPQ, Communication Service Providers (CSPs) have the potential to save expenses associated with customisation and maintenance, while also accelerating their time-to-market. In order to assess the practical ramifications of Salesforce Industries CPQ, the researcher intends to deploy the aforementioned system within ABC Insurance Company. The objective of the study is to evaluate the program's influence on sales efficiency and profitability by conducting interviews with a targeted cohort of employees who have employed the software in their sales endeavours. Furthermore, the study will include hypothesis testing to analyse the timing of quotation production across various policy configurations utilising Salesforce Industries CPQ, aiming to provide further validation of its benefits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

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2023 China Automation Congress, CAC 2023

作者： Li, Jiazhuo Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru The School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland The Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey The Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798350303759

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results. © 2023 IEEE.

关键词： Convolution

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Cloud Energy Storage Management Including Smart Home Physical Parameters

Cloud Energy Storage Management Including Smart Home Physica...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Vikash Kumar Saini Srinivas Yelisetti Rajesh Kumar Ameena S. Al-Sumaiti Center for Energy and Environment Malaviya National Institute of Technology Jaipur Advanced Power and Energy Center Electrical Engineering and Computer Science Khalifa University UAE Department of Electrical Engineering Malaviya National Institute of Technology Jaipur

Consumption of green energy in residential communities is increasing compared to conventional supply. However, the variability in generation due to different weather parameters is a significant challenge to their growth rate. Energy storage has the potential to address this issue, and sharing economy-based cloud energy storage (CES) has gained popularity as a way to reduce energy consumption costs and increase revenue. This study analyzes the deployment of CES infrastructure operation in seven residential smart homes community. All seven homes utilized CES services based on their daily net load demand. In the first phase, the PSO algorithm optimized the load trajectory, taking into account all smart home parameters and physical components. Subsequently, the operational cost of CES in the community is evaluated in scenarios with and without home energy management systems. Simulation results indicated that the energy consumption cost of the residential community from the grid is zero, and revenue is 43.59% higher when home energy management systems are employed. The modeled home parameters significantly affected total community energy costs.

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Short-Term Load Forecasting Employing Recurrent Neural Networks

Short-Term Load Forecasting Employing Recurrent Neural Netwo...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Tanzim Mostafa Mostafa M. Fouda Mohammad G. Abdo Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Idaho National Laboratory Idaho Falls ID USA

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

This research paper explores the domain of Short-Term Load Forecasting (STLF) through the application of Long Short- Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. Focused on the west region of the Electric Reliability Council of Texas (ERCOT), the study employs a dataset spanning 2012–2015, enriched with new features such as day of the month, month of the year, and a detailed holiday classification. The dataset also incorporates hourly temperature, humidity, time of the day, day of the week, and hourly load values, offering a comprehensive perspective for load forecasting. Through meticulous hyperparameter tuning, LSTM and GRU models are optimized to find their best architecture. Four evaluation metrics have been employed to assess the performance of our models, namely, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination or R Squared (R2). With the identified optimal hyperparameters and look-back window, the obtained values for MAE, RMSE, MAPE and R2 for the LSTM model are 8.94, 12.24, 0.80 and 0.9973, respectively. The GRU model also achieves good performance with MAE, RMSE, MAPE, and R2 of 10.72, 14.48, 0.96, and 0.9962, respectively. Our research shows that the accuracy of STLF on the dataset has been improved by adding new features and fine-tuning the LSTM and GRU models, with the LSTM model achieving the best performance.

关键词： Measurement Recurrent neural networks Load forecasting Predictive models Logic gates Reliability Root mean square

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