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Advancements in Sentiment analysis: A Deep Learning Approach 15

Advancements in Sentiment Analysis: A Deep Learning Approach

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15th IEEE International conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Kallam, Yogeshvar Reddy Panchumarthi, Lovely Yeswanth Parchuri, Lavanya Hajarathaiah, Koduru Enduri, Murali Krishna Anamalamudi, Satish SRM University ACT Lab Department of CSE AP India SRM University Networks Lab Department of CSE AP India

ISBN: (纸本)9798350324433

Sentiment analysis, a pivotal discipline in the digital era, revolves around the nuanced task of categorizing user sentiments within textual data. This research embarks on an exhaustive exploration of diverse sentiment analysis models, comprising Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), Support Vector Machines (SVMs), and a Baseline Model. Through a rigorous comparative analysis of their performance across varied datasets, this study illuminates the unique strengths and limitations inherent to each model. Furthermore, the research extends beyond the realm of academic inquiry to unveil the practical applications of sentiment analysis. It underscores the profound impact of sentiment analysis in contemporary datadriven decision-making, illustrating its significance across multifaceted domains such as marketing, social media monitoring, finance, customer service, and public sentiment analysis. This investigation seeks to empower stakeholders with invaluable insights, thereby facilitating informed choices and strategies in the ever-evolving digital landscape. © 2023 IEEE.

关键词： Sentiment analysis

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Noisy Agents: Self-supervised exploration by Predicting Auditory Events

Noisy Agents: Self-supervised Exploration by Predicting Audi...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Gan, Chuang Chen, Xiaoyu Isola, Phillip Torralba, Antonio Tenenbaum, Joshua B. MIT Cambridge MA 02139 USA MIT IBM Watson AI Lab Cambridge MA 02142 USA Tsinghua Univ Beijing Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Humans integrate multiple sensory modalities (e.g., visual and audio) to build a causal understanding of the physical world. In this work, we propose a novel type of intrinsic motivation for Reinforcement Learning (RL) that encourages the agent to understand the causal effect of its actions through auditory event prediction. First, we allow the agent to collect a small amount of acoustic data and use K-means to discover underlying auditory event clusters. We then train a neural network to predict the auditory events and use the prediction errors as intrinsic rewards to guide RL exploration. We first conduct proof-of-concept experiments using a set of Atari games for an in-depth analysis of our module. We then apply our model to embodied audio-visual exploration using the Habitat simulator and active exploration with a rolling robot using the ThreeDWorld (TDW) simulator. Experimental results demonstrate the advantages of using audio signals over vision-based models as intrinsic rewards to guide RL explorations.

关键词： visualization Neural networks Reinforcement learning Games Robot sensing systems Acoustics Noise measurement

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ARViz: Interactive visualization of Association Rules for RDF data exploration 25

ARViz: Interactive Visualization of Association Rules for RD...

引用

25th International conference Information visualisation (iv) - AI and visual Analytics and data Science

作者： Menin, Aline Cadorel, Lucie Tettamanzi, Andrea Giboin, Alain Gandon, Fabien Winckler, Marco Univ Cote Azur INRIA CNRS I3S UMR 7271 Nice France

ISBN: (纸本)9781665438278

Association rule mining often leads the analyst into a rough rummaging process to identify rules that are relevant to understand specific problems. We propose a visualization interface to assist the rule selection process and evaluate it on an RDF knowledge graph derived from the COVID-19 Open Research dataset. The user interface supports data exploration with focus on the overview of rules through a scatter plot, subsets of rules through a chord diagram chart, and itemsets through an association graph which is dynamically created by entering an item of interest (i.e. a named entity). Further, the analyst can interactively recover a list of publications containing the named entities involved in a particular rule. Among the original aspects of our approach, we highlight the representation of attributes describing measures of interest (i.e. confidence and interestingness), a visual indication of existence (or not) of symmetry in association rules, the exploration of subsets of rules according to clusters of publications and named entities, and an interactive prompting that aims at expanding the discovery of named entities within selected association rules. We assess our approach through a semi-structured interview involving experts in the domains of data mining and biomedicine, whose feedback could assist the refinement of the visual and interaction tools.

关键词： association rule mining interactive visualization knowledge graph visualization RDF visualization COVID-19

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VerseMeet - A Virtual Meeting Application 10

VerseMeet - A Virtual Meeting Application

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10th International conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sarumathi, S. Gokul, M. Gokulavelan, M. Shimar Roshan, R.S. K.S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Virtual Reality (VR) has emerged as a potent technological tool for elevating remote collaboration and immersive communication experiences. This paper introduces a sophisticated VR meeting and collaboration application, harnessing the capabilities of VR headsets to craft dynamic and interactive virtual meeting environments. The application seamlessly integrates a diverse array of features, including a collaborative sketch board fostering visual interaction, an embedded web browser for streamlined online exploration, and the ability to import 3D models for real-time visualization. Additionally, strategic plans are in place to implement an avant-garde 3D visualization feature aimed at enhancing data analytics capabilities within the platform. The application offers users the functionality to record and review their sessions. Noteworthy is the adaptive nature of our VR meeting app, tailoring its environment based on the number of users, thereby promoting a distinctive collaborative experience. Developed utilizing VRTK, Unity 3D, and the Photon Engine, this application functions as a standalone system, obviating the necessity for a PC-to-headset intermediary. This paper delves comprehensively into the design, implementation, and development of our VR meeting application, shedding light on its unique capabilities and its potential to revolutionize remote collaboration and data analysis. © 2024 IEEE.

关键词： Virtual environments

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Research on Key Technologies for Enhancing Image analysis Capability of Power Transmission and Transformation Equipment Based on Large visual Models 10

Research on Key Technologies for Enhancing Image Analysis Ca...

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IEEE 10th International conference on Edge Computing and Scalable Cloud (IEEE EdgeCom) / 11th IEEE International conference on Cyber Security and Cloud Computing (IEEE CSCloud)

作者： Zhang, Wangjun Chen, Yu Wu, Xiong Li, Fan Yang, Hongshan State Grid Shanghai Elect Power Co Shanghai Peoples R China ShanghaiCo Ltd Transwarp Technol Shanghai Peoples R China

ISBN: (纸本)9798350377149;9798350377132

This study significantly enhances the accuracy and efficiency of image analysis for power transmission and transformation equipment by introducing large model technology, bringing innovation to the field of intelligent inspection for power systems. Through an in-depth exploration of key aspects such as data preparation, model selection and training, and image analysis processing, this research employed a pre-trained large model based on the Transformer architecture. It was meticulously fine-tuned and trained according to the characteristics of power equipment images. By preprocessing and augmenting a vast collection of power transmission and distribution equipment images under various conditions, the model successfully extracted features, achieving high-precision defect identification and classification. The model was evaluated using cross-validation and an independent test set method, confirming its superiority over traditional image processing and existing intelligent inspection systems through metrics such as accuracy, recall, and F1 score. Experimental results have shown that this research method significantly outperforms traditional techniques and existing technologies on key performance indicators. Despite significant accomplishments, there remains room for enhancing the model's ability to generalize and adapt to intricate environments. Upcoming efforts will delve into streamlined model training techniques and refine the model's architecture to handle more intricate power image scenarios. Furthermore, the fusion of large model technology with other cutting-edge technologies will be explored to enable more astute, adaptable, and real-time scrutiny of power equipment. This study showcases the vast potential and practical worth of large model technology in power image analysis and intelligent inspection, poised to propel the intelligent evolution of the power sector.

关键词： Large Model Power Equipment Image analysis Transformer Architecture

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visual analysis of Voice in Crossover Singing

Visual Analysis of Voice in Crossover Singing

引用

International conference on Information visualisation (iv)

作者： Jie Hua Wei Yi Faculty of Information Engineering Shaoyang University Hunan China Department of MCCALL Macquarie University Sydney Australia

The current research on acoustic properties in singing voice analysis has mainly focused on individual song segments, and analysed them using simple data tables and basic charts. However, there has been limited exploration of comparing data from multiple sources, and visual analysis in crossover singing has been either too simplistic or too complex to provide a comprehensive view. This study aims to address this gap by incorporating song segments from different musical styles and utilising an innovative graph drawing method to generate interactive graphs for comprehensive musical data analysis. The findings provide additional support for existing statements in the field of musical data analysis, and demonstrate the effectiveness of the proposed graph method for analysing multiple song segments. At this stage, the study's findings confirm that the formant frequency $F_exploration$ of singing across styles is less modified, but $F_analysis-F_analysis$ varies in styles singing in English. Additionally, the formant frequency in Mandarin and Cantonese singing may be associated with pitch. The study also identifies that visualised graphs can produce similar results as current vocal research and are convenient for reading multiple data simultaneously. The methodology has the potential to be extended to other areas of musical visualisation to uncover insights from complex music datasets.

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Literature Review on the Current State-of-the-Art in Research and Technological Advancements in the Field of Machine Learning Applied to Predictive Maintenance 43rd

Literature Review on the Current State-of-the-Art in Researc...

引用

43rd IFIP WG 5.7 International conference on Advances in Production Management Systems (APMS)

作者： Resanovic, Daniel Balc, Nicolae Univ Appl Sci CAMPUS 02 Graz Austria Tech Univ Cluj Napoca Cluj Napoca Romania

ISBN: (纸本)9783031716355;9783031716331;9783031716324

This literature review examines the technologies of Machine Learning (ML) in Predictive Maintenance (PdM), highlighting the necessity for industries to boost production efficiency for competitive advantage amid growing global demands. It underscores the pivotal roles of technological advancements, especially the Internet of Things (IoT) and Big data, in enabling smarter automation and intelligent production processes. By leveraging equipment sensor data, Predictive Maintenance serves as a crucial proactive maintenance strategy, enhancing reliability and accuracy through its diagnostic and prognostic stages. However, challenges in financial, organizational, data, and repair complexities hinder its full-scale implementation, necessitating a deeper understanding of ML techniques in PdM. The review further discusses AI/ML's role in enhancing predictive maintenance, detailing applications of AI, ML, and Deep Learning (DL) in predictive analytics and identifying emerging trends like transformers and self-supervised learning, which promise to improve PdM outcomes. Through a structured analysis, this report underscores the evolving landscape of ML applications in PdM, highlighting both challenges and opportunities, and suggesting further exploration of ML algorithms in this field.

关键词： Predictive Maintenance Machine Learning Artificial Intelligence State of ML in Predictive Maintenance data Management

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Supporting Serendipitous Discovery and Balanced analysis of Online Product Reviews with Interaction-Driven Metrics and Bias-Mitigating Suggestions 22

Supporting Serendipitous Discovery and Balanced Analysis of ...

引用

CHI conference on Human Factors in Computing Systems (CHI)

作者： Jasim, Mahmood Collins, Christopher Sarvghad, Ali Mahyar, Narges Univ Massachusetts Amherst MA 01003 USA Ontario Tech Univ Oshawa ON Canada

ISBN: (纸本)9781450391573

In this study, we investigate how supporting serendipitous discovery and analysis of online product reviews can encourage readers to explore reviews more comprehensively prior to making purchase decisions. We propose two interventions - exploration Metrics that can help readers understand and track their exploration patterns through visual indicators and a Bias Mitigation Model that intends to maximize knowledge discovery by suggesting sentiment and semantically diverse reviews. We designed, developed, and evaluated a text analytics system called Serendyze, where we integrated these interventions. We asked 100 crowd workers to use Serendyze to make purchase decisions based on product reviews. Our evaluation suggests that exploration metrics enabled readers to efficiently cover more reviews in a balanced way, and suggestions from the bias mitigation model influenced readers to make confident data-driven decisions. We discuss the role of user agency and trust in text-level analysis systems and their applicability in domains beyond review exploration.

关键词： serendipity product review exploration bias mitigation model

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Beyond Generating Code: Evaluating GPT on a data visualization Course 1

Beyond Generating Code: Evaluating GPT on a Data Visualizati...

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1st IEEE VIS Workshop on visualization Education, Literacy, and Activities (EduVis) part of the IEEE VIS conference

作者： Chen, Zhutian Zhang, Chenyang Wang, Qianwen Troidl, Jakob Warchol, Simon Beyer, Johanna Gehlenborg, Nils Pfister, Hanspeter Harvard Univ Sch Engn & Appl Sci John A Paulson Sch Engn & Appl Sci Boston MA USA Harvard Boston MA USA Harvard Med Sch Biomed Informat Boston MA USA

ISBN: (纸本)9798350330304

This paper presents an empirical evaluation of the performance of the Generative Pre-trained Transformer (GPT) model in Harvard's CS171 data visualization course. While previous studies have focused on GPT's ability to generate code for visualizations, this study goes beyond code generation to evaluate GPT's abilities in various visualization tasks, such as data interpretation, visualization design, visual data exploration, and insight communication. The evaluation utilized GPT-3.5 and GPT-4 through the APIs of OpenAI to complete assignments of CS171, and included a quantitative assessment based on the established course rubrics, a qualitative analysis informed by the feedback of three experienced graders, and an exploratory study of GPT's capabilities in completing border visualization tasks. Findings show that GPT-4 scored 80% on quizzes and homework, and Teaching Fellows could distinguish between GPT- and human-generated homework with 70% accuracy. The study also demonstrates GPT's potential in completing various visualization tasks, such as data cleanup, interaction with visualizations, and insight communication. The paper concludes by discussing the strengths and limitations of GPT in data visualization, potential avenues for incorporating GPT in broader visualization tasks, and the need to redesign visualization education.

关键词： visualization

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A SPATIO-TEMPORAL visual ANALYTICS TOOL FOR SIMULATED TRANSPORT ORIENTED-LAND USE URBAN PLANNING data 7

A SPATIO-TEMPORAL VISUAL ANALYTICS TOOL FOR SIMULATED TRANSP...

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7th International conference on Smart data and Smart Cities, SDSC 2022

作者： Merchan Zorro, L. Vivas Meza, A.A. Bautista Acosta, O.D. Castro Barrera, H.E. Peña, J.R. Guzman, L.A. Hernandez, J.T. Imagine Research Group Sur Research Group University de Los Andes Colombia

This article presents a proposal framed in urban visual analytics, which aims to facilitate the exploration and analysis of zonal spatial indicators over time for a case of a large city-region. The context in which this exploration is carried out is the evaluation of the impact of interventions in land use and transportation, based on data produced by an integrated simulation system. The large amount of data to evaluate a scenario makes urgent the effort to design new interactive analysis environments. The resulting prototype (VIZ-BUS: visual Analytics for Bogota Urban Simulator) has, for its design, an analyst-centric approach to the analysis task. Our first web deployment has been very well received by urban planning specialists in order to evaluate scenarios for a sustainable and smart city-region. © 2022 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： data visualization

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