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2024 Photonics and Electromagnetics Research symposium, PIERS 2024

作者： You, J. Zhao, Z. Zheng, X. National Innovation Institute of Defense Technology Beijing100071 China National University of Defense Technology Changsha410073 China

ISBN: (纸本)9798350375909

In this paper, we introduce a powerful model agnostic data enhancement (MADE) learning algorithm to realize the efficient prediction and rapid inverse design of 2D diffractive chiral metamaterials. In order to vividly demonstrate the performance of our MADE algorithm, multi-shaped chiral structures with various geometrical parameters (e.g., width, separation space, module length, depths) and material components are selected. To generate the training data for MADE model, we utilize the conventional method of rigorous coupled wave analysis (RCWA) to capture the chiroptical response of metamaterials. Moreover, data enhancement and multi-task learning strategy constitute two key sections of MADE algorithm. First, a forward prediction network replying on data enhancement is built to generate large amount of "pseudo data"to reinforce the original training dataset that is much less than previous work, which can also perform fast prediction of the metamaterials' circular dichroism response. Additionally, multi-task learning strategy is exploited in MADE algorithm to address the issue that various geometric parameters apparently affect the chiroptical response in a different manner, enhancing the model versatility. The comprehensive results demonstrate that our MADE algorithm can not only carry out the forward prediction for metamaterials' chiroptical response with a significantly-fast computational speed that is 4 orders of magnitude faster than RCWA, but equips with fast inverse design function for on-demand metamaterials, which also can be applied to contrive new structures with desired optical response. This study set out to assess the feasibility of deep learning method in the large-scale, efficient design of 2D diffractive chiral metamaterials and more sophisticated photonic devices on chip. © 2024 ieee.

关键词： Photonic devices

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EmoSpeech: A Corpus of Emotionally Rich and Contextually Detailed Speech Annotations 14

EmoSpeech: A Corpus of Emotionally Rich and Contextually Det...

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14th International symposium on Chinese Spoken Language Processing

作者： Bian, Weizhen Zhou, Yubo Zhang, Kaitai Gu, Xiaohan Hong Kong Univ Sci & Technol Hong Kong Peoples R China Hong Kong Baptist Univ Hong Kong Peoples R China

ISBN: (纸本)9798331516833;9798331516826

Advances in text-to-speech (TTS) technology have significantly improved the quality of generated speech, closely matching the timbre and intonation of the target speaker. However, due to the inherent complexity of human emotional expression, the development of TTS systems capable of controlling subtle emotional differences remains a formidable challenge. Existing emotional speech databases often suffer from overly simplistic labelling schemes that fail to capture a wide range of emotional states, thus limiting the effectiveness of emotion synthesis in TTS applications. To this end, recent efforts have focussed on building databases that use natural language annotations to describe speech emotions. However, these approaches are costly and require more emotional depth to train robust systems. In this paper, we propose a novel process aimed at building databases by systematically extracting emotion-rich speech segments and annotating them with detailed natural language descriptions through a generative model. This approach enhances the emotional granularity of the database and significantly reduces the reliance on costly manual annotations by automatically augmenting the data with high-level language models. The resulting rich database provides a scalable and economically viable solution for developing a more nuanced and dynamic basis for developing emotionally controlled TTS systems.

关键词： Targeted Speech Extraction Emotion Recognition Automatic Speech Recognition large Language Model Speech analysis

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ARTIFACT IDENTIFICATION IN DIGITAL HISTOPATHOLOGY IMAGES USING FEW-SHOT LEARNING 19

ARTIFACT IDENTIFICATION IN DIGITAL HISTOPATHOLOGY IMAGES USI...

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19th ieee International symposium on Biomedical Imaging (ieee ISBI)

作者： Shaikh, Nazim N. Wasag, Kamil Nie, Yao Roche Diagnost Solut Computat Sci & Informat Computat Pathol Lab Santa Clara CA 95050 USA

ISBN: (纸本)9781665429238

The advent of deep learning methods has led to breakthroughs in many digital histopathology image analysis tasks. However, automatic analysis is often impacted by the presence of various artifacts introduced during different tissue and slide processing stages. Therefore, it is desirable to have a generic artifacts identification algorithm to automatically exclude the artifacts regions in the downstream analysis. In this paper, considering the wide diversity of artifacts that present in histopathology images, and the difficulty to obtain a large amount of training data, we frame the artifacts identification task as a tile-based image classification problem and explore the feasibility of using a few-shot learning technique, specifically, prototypical network, for the task. We demonstrate that the use of prototypical network can effectively identify image tiles that contain various artifacts using a very small set of training images. The trained model is also able to generalize well to unseen artifacts. We validate the approach by applying it on both immunohistochemistry and H&E stained tissues images, showing that it is a more favorable approach compared to standard transfer learning for this application.

关键词： Histopathology image analysis artifact detection tile-based image classification transfer learning few-shot learning

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analysis and Prediction of Lettuce Crop Yield in Aeroponic Vertical Farming using Logistic Regression Method 2

Analysis and Prediction of Lettuce Crop Yield in Aeroponic V...

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2nd International Conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Gowtham, R. Jebakumar, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu Tamil Nadu Kattankulathur India

ISBN: (纸本)9781665491990

Human population growth strains food supplies, resulting in more than one billion of the world's 6.5 billion people facing hunger, which is the leading cause of death. In consequence, increasing crop production through traditional farming methods will not address the global food scarcity problem. As a precision agriculture approach, aeroponics, a soilless agriculture technique, can assist in cultivating in-demand food crops, which will help serve the people. Because aeroponics is an indoor farming technology, factors such as pH, EC, light, temperature, PPM, and turbidity will affect crop growth. Hence, monitoring an aeroponic crop growth system efficiently is crucial for achieving a high crop yield and reducing the need for food crops. Even though, many crop monitoring systems use IoT and machine learning algorithms, their prediction accuracy is not up to the level and if they are deployed in the real world, they may not meet customer expectations. In order to efficiently monitor crops, more advanced models are needed. However, understanding the large volume of data can be very tedious. data visualization techniques can help make the distribution of the dataset easier to understand. Machine learning methods and packages can be used for environmental monitoring and visualization of the data where, the collection of crop growth dataset is carried out using various IoT sensors deployed in the growth environment. This study provides a comprehensive explanation of the data visualization techniques used to study the lettuce crop dataset and predict lettuce yield using the logistic regression machine learning method which is been implemented using Python coding language. © 2023 ieee.

关键词： Lettuce

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RASED: A Scalable Dashboard for Monitoring Road Network Updates in OSM 23

RASED: A Scalable Dashboard for Monitoring Road Network Upda...

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23rd ieee International Conference on Mobile data Management (ieee MDM)

作者： Musleh, Mashaal Mokbel, Mohamed F. Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (数字)9781665451765

ISBN: (纸本)9781665451765

Understanding the evolution and changes of digital road networks and how it resembles the true physical road network, have been a rich area of study within map analyzers, urban planners, and transportation communities. The main focus was to study OpenStreetMap (OSM) as the most commonly used platform for worldwide digital road networks, and is deemed even more accurate than commercial maps. However, all such studies have been localized to small areas of interest, mainly due to the large scale of the whole OSM road network. This paper presents RASED;a publicly available scalable dashboard to interactively monitor and analyze the evolution of all OSM road network. Using RASED, map analyzers can query and visualize various statistics about the road network daily changes worldwide, which would give a better understanding of the status of map quality and stability anywhere in the world. RASED relies on daily and monthly offline precomputations, accessed via a hierarchical temporal index structure. Experimental results show that RASED queries are always supported in the order of milliseconds, regardless of how large is the query temporal window, which allows highly interactive map analysis.

关键词： Roads Stability criteria Transportation data visualization Indexes Monitoring

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InsightLens: Augmenting LLM-Powered data analysis With Interactive Insight Management and Navigation

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ieee Transactions on visualization and Computer Graphics 2025年第6期31卷 3719-3732页

作者： Luoxuan Weng Xingbo Wang Junyu Lu Yingchaojie Feng Yihan Liu Haozhe Feng Danqing Huang Wei Chen State Key Lab of CAD&CG Zhejiang University Hangzhou China Weill Cornell Medical College Cornell University Ithaca NY USA Tencent Inc. Shenzhen China

The proliferation of large language models (LLMs) has revolutionized the capabilities of natural language interfaces (NLIs) for data analysis. LLMs can perform multi-step and complex reasoning to generate data insights based on users’ analytic intents. However, these insights often entangle with an abundance of contexts in analytic conversations such as code, visualizations, and natural language explanations. This hinders efficient recording, organization, and navigation of insights within the current chat-based LLM interfaces. In this paper, we first conduct a formative study with eight data analysts to understand their general workflow and pain points of insight management during LLM-powered data analysis. Accordingly, we introduce InsightLens, an interactive system to overcome such challenges. Built upon an LLM-agent-based framework that automates insight recording and organization along with the analysis process, InsightLens visualizes the complex conversational contexts from multiple aspects to facilitate insight navigation. A user study with twelve data analysts demonstrates the effectiveness of InsightLens, showing that it significantly reduces users’ manual and cognitive effort without disrupting their conversational data analysis workflow, leading to a more efficient analysis experience.

关键词： data analysis data visualization Navigation Oral communication Recording Codes Natural languages Semantics Organizations Manuals

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Automated Recovery and visualization of Test-to-Code Traceability (TCT) Links: An Evaluation

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ieee ACCESS 2021年 9卷 40111-40123页

作者： Aljawabrah, Nadera Gergely, Tamas Misra, Sanjay Fernandez-Sanz, Luis Univ Szeged Dept Software Engn H-6720 Szeged Hungary Atilim Univ Dept Comp Engn TR-06830 Ankara Turkey Covenant Univ Dept Elect & Informat Engn Ota 112233 Nigeria Univ Alcala Dept Comp Sci Madrid 28801 Spain

In the software development process, traceability links between unit tests and code are not explicitly maintained, and dependencies in most cases are manually identified. As a result, a large amount of effort and time is required during the comprehension process to establish the links between these artifacts. Although there are several methods that can infer such links based on different phenomenons, these methods usually produce different set of traceability links. This work expands upon previous traceability link recovery and visualization studies by implementing a combination of traceability recovery methods that automatically retrieve the links, and visualizing them to help developers to overview the links inferred by various recovery techniques, and also to select the right relations for analyses. The results of the usability study show that the visualization model presented herein can effectively support browsing, comprehension, and maintenance of Test-to Code Traceability (TCT) links in a system with enhanced efficiency, as well as visualization of TCT links from multiple sources is better than a visualization of single source of traceability links.

关键词： visualization Software Tools Hypertext systems Task analysis Maintenance engineering data visualization Software development software comprehension test case traceability links visualization

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An Imperial analysis of large Language Models for Automated Tweet Sentiment Prediction

An Imperial Analysis of Large Language Models for Automated ...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Akhouri, Shivam Ajbani, Vidhi Lakshminarayanan, Ritika Pandey, Trilok Nath Nigam, Meher Shrishti Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350313987

Company' s brand perception majorly depends on customer experience and the reviews which follow that. A customer is capable of influencing many more people just on the basis of reviews given by him/her. Google released Pathways Language Model (PaLM) which is a major advancement in Artificial Intelligence (AI). It has been trained with the Pathways System, which allows it to generalize tasks in various domains. In this work, a trustworthy platform is provided for the examination of millions of people's continually moving and changing perspectives. Twitter data is captured, and effective sentiment and data analysis is used to generate trustworthy and helpful info graphics reflecting public opinion. Product sales, stock returns, election outcomes, and other commercial and social events may all be predicted and explained using the information found in tweets. Brands, product manufacturers, and other companies may utilise the information derived from data analysis to better understand their brand image, expand their market share by targeting the relevant demographics at the right moments, and enhance their offerings in terms of both quality and customer service. © 2023 ieee.

关键词： Attention Units data visualization Masks Sentiment analysis Tokenization

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Waltzboard: Multi-Criteria Automated Dashboard Design for Exploratory analysis

Waltzboard: Multi-Criteria Automated Dashboard Design for Ex...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Jiwon Choi Jaemin Jo Sungkyunkwan University

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

We present Waltzboard, an automated dashboard design system for exploratory data analysis. Despite the benefit of dashboards, which provide a glanceable overview of data, previous dashboard design systems often require precomputation, such as training deep-learning models, and do not adapt effectively to changes in the user’s intent during data analysis, hindering quick and flexible data exploration. To overcome these challenges, we introduce a dashboard evaluation framework that quantifies how a dashboard describes data in terms of five key measures: Specificity, Interestingness, Diversity, Coverage, and Parsimony. We then present a three-phase search algorithm designed to efficiently explore dashboard designs without the need for precomputation. Finally, we present a user interface that allows the user to dynamically build their own intent and reason for the design process. The result of our performance benchmark and user study demonstrates that Waltzboard not only designs a more effective dashboard within seconds but also supports flexible exploratory data analysis to meet diverse analytic needs.

关键词： Training Surveys Analytical models Adaptation models data analysis Heuristic algorithms data visualization Benchmark testing data models

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Learning Task-Agnostic Action Spaces for Movement Optimization

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第12期28卷 4700-4712页

作者： Babadi, Amin van de Panne, Michiel Liu, C. Karen Hamalainen, Perttu Aalto Univ Dept Comp Sci Espoo 02150 Finland Univ British Columbia Dept Comp Sci Vancouver BC V6T 1Z4 Canada Stanford Univ Dept Comp Sci Stanford CA 94305 USA

We propose a novel method for exploring the dynamics of physically based animated characters, and learning a task-agnostic action space that makes movement optimization easier. Like several previous article, we parameterize actions as target states, and learn a short-horizon goal-conditioned low-level control policy that drives the agent's state towards the targets. Our novel contribution is that with our exploration data, we are able to learn the low-level policy in a generic manner and without any reference movement data. Trained once for each agent or simulation environment, the policy improves the efficiency of optimizing both trajectories and high-level policies across multiple tasks and optimization algorithms. We also contribute novel visualizations that show how using target states as actions makes optimized trajectories more robust to disturbances;this manifests as wider optima that are easy to find. Due to its simplicity and generality, our proposed approach should provide a building block that can improve a large variety of movement optimization methods and applications.

关键词： Optimization Task analysis Aerospace electronics Trajectory optimization Reinforcement learning Training Splines (mathematics) Movement optimization trajectory optimization policy optimization hierarchical reinforcement learning action space

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