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5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Abdullah Aswad, Salma College of Science University of Duhok Department of Computer Science Duhok42001 Iraq

ISBN: (纸本)9798350337525

The central theme for this thesis is the design of an aspect-based sentiment analysis model for the classification of online Italian automotive forums' comments. The work starts with designing a strategy for collecting information about target forums to make it possible to develop a machine learning-based sentiment classification model. The study involved applying the CNN and LTSM model, a state-of-the-art solution based on a parametric model that will improve the performance of a baseline algorithm, especially in case of very noisy data like the ones where this tool is supposed to be to work on. This work has been designed as a two-stage CNN and LTSM classifier in all its parts. It was compared with a one-step classifier to detect the pertinence about some topics, and eventually, the sentiment achieved an accuracy of 96.78% for all comments. The current problem passed from a typical three degrees' polarity sentiment analysis to a four labels text classification, where it will be introduced an additional category for determining whether the text is pertinent to a particular topic or not. Presenting this information, the models must be enhanced, and a cascade classification solution will be proposed. The final model is then utilized for a real-world use case. New data have been classified concerning some selected topics, finally presented exploiting a data visualization but still not satisfactory, thus making sentiment analysis an ongoing and open research subject. © 2023 ieee.

关键词： Sentiment analysis

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Global Patent Landscape of Decision Support System in The Business: An Overview 8

Global Patent Landscape of Decision Support System in The Bu...

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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Irawan, Michelia Champaca Salsabila Purnomo, Agung Sanjaya, Angelie Natalia Meiryani Ubud, Sahnaz Maulana, Fairuz Iqbal Binus Business School Undergraduate Program Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia School of Accounting Bina Nusantara University Accounting Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350326093

The study of decision support systems in business and entrepreneurial ecosystems continues to grow and play an essential role. However, few studies have not comprehensively reviewed decision support system studies from the patent perspective. This research attempts to review the global patent landscape and the main trends of patents on decision support systems in business. Research using patent landscapes analysis was intended to clarify data visualization, identification, and analysis. This study uses 6,713 patent documents and 3,492 simple patent families over 35 years from the *** database. The study results reveal increased trends regarding developments to patent activity related to decision support systems in the business. There were business opportunities that business parties could use to the potential for increasing patent filings. The CPC codes that dominate decision support system patents in the business were G (physics). The United States was the most prolific country regarding owners and inventors. © 2023 ieee.

关键词： Decision support systems

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Contrastive Graph Representations for Logical Formulas Embedding

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ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第4期35卷 3563-3574页

作者： Lin, Qika Liu, Jun Zhang, Lingling Pan, Yudai Hu, Xin Xu, Fangzhi Zeng, Hongwei Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Prov Key Lab Satellite & Terr Network Tech Natl Engn Lab Big Data Analyt Xian 710049 Shaanxi Peoples R China

Currently, the non-transparent computing process of deep learning has become a significant reason hindering its further development. The Neural-Symbolic (NS) system formed by integrating logic rules into neural networks has attracted increasing attention owing to its direct interpretability. Embedding symbolic logical formulas into a low-dimensional continuous space provides an effective way for the NS system. However, current studies are all constrained by the modeling ability for its syntactic structure and fail to preserve the intrinsic semantics in embeddings, which causes poor performance on downstream reasoning tasks. To this end, this paper proposes a novel method of Contrastive Graph Representations (ConGR) for logical formulas embedding. First, to improve the modeling ability for the syntactic structure, ConGR introduces a densely connected graph convolutional network (GCN) with an attention mechanism to process syntax parsing graphs of formulas. In this way, discriminative local and global embeddings of formulas are obtained at the syntax level. Second, the contrastive instances (positive or negative) for each anchor formula are generated by the transformation under the guidance of logical properties. To preserve semantic information, two types of contrast, global-local and global-global, are carried out to refine formula embeddings. Extensive experiments demonstrate that ConGR obtains superior performance against state-of-the-art baselines on entailment checking and premise selection datasets.

关键词： Syntactics Semantics Task analysis visualization Transforms Recurrent neural networks Deep learning Formulas embedding graph representation contrastive learning graph convolutional network

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A Machine Learning based Platform for Remote Management of Heart Failure Patients 19

A Machine Learning based Platform for Remote Management of H...

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19th ieee International Conference on Intelligent Computer Communication and Processing Conference, ICCP 2023

作者： Man, Rares-Ioan Anghel, Ionut Technical University of Cluj-Napoca Computer Science Department Cluj-Napoca Romania

ISBN: (纸本)9798350370355

Heart failure is a growing concern due to its high incidence nowadays, also representing a major cause of morbidity and mortality worldwide. In this paper we propose a web-based platform that incorporates both the clinical data prediction aspect and the continuous monitoring of the heart health. We implement multiple machine learning models that can support the doctors in the process of classification between a healthy and unhealthy situation. The platform benefits from an ETL (Extract, Transform, Load) sub-system that processes biometric data from smart wearables and displays it in customizable dashboards for a more illustrative visualization. The prediction service integrates three Machine Learning (ML) techniques, namely Logistic Regression, Naïve Bayes Classifier, and a custom Artificial Neural Network responsible for making classifications on the monitored data. The results illustrate that the proposed solution's usage for performing remote monitoring and heart health assessment is feasible, obtaining promising accuracies with the aid of a public heart failure dataset (best accuracy of 88.5%). © 2023 ieee.

关键词： Machine learning

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Memorial GAN With Joint Semantic Optimization for Unpaired Image Captioning

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ieee TRANSACTIONS ON CYBERNETICS 2023年第7期53卷 4388-4399页

作者： Song, Peipei Guo, Dan Zhou, Jinxing Xu, Mingliang Wang, Meng Hefei Univ Technol Key Lab Knowledge Engn Big Data Intelligent Interconnected Syst Lab Anhui Prov Minist EducSch Comp Sci & Informat Engn Hefei 230601 Peoples R China Hefei Univ Technol Sch Artificial Intelligence Hefei 230601 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China

Most works of image captioning are implemented under the full supervision of paired image-caption data. Limited to expensive cost of data collection, the task of unpaired image captioning has attracted researchers' attention. In this article, we propose a novel memorial GAN (MemGAN) with the joint semantic optimization for unpaired image captioning. The core idea is to explore implicit semantic correlation between disjointed images and sentences through building a multimodal semantic-aware space (SAS). Concretely, each modality is mapped into a unified multimodal SAS, where SAS includes the semantic vectors of image I, visual concepts O, unpaired sentence S, and the generated caption C. We adopt the memory unit based on multihead attention and relational gate as a backbone to preserve and transit crucial multimodal semantics in the SAS for image caption generation and sentence reconstruction. Then, the memory unit is embedded into a GAN framework to exploit the semantic similarity and relevance in SAS, that is, imposing a joint semantic-aware optimization on SAS without supervision clues. To summarize, the proposed MemGAN learns the latent semantic relevance of SAS's multimodalities in an adversarial manner. Extensive experiments and qualitative results demonstrate the effectiveness of MemGAN, achieving improvements over state of the arts on unpaired image captioning benchmarks.

关键词： Semantics Synthetic aperture sonar visualization Task analysis Optimization Generative adversarial networks Correlation Generative adversarial network memory network semantic-aware space (SAS) unpaired image captioning

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Self-Supervised Color-Concept Association via Image Colorization

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 247-256页

作者： Hu, Ruizhen Ye, Ziqi Chen, Bin Van Kaick, Oliver Huang, Hui Shenzhen University Visual Computing Research Center China Carleton University School of Computer Science Canada

The interpretation of colors in visualizations is facilitated when the assignments between colors and concepts in the visualizations match human's expectations, implying that the colors can be interpreted in a semantic manner. However, manually creating a dataset of suitable associations between colors and concepts for use in visualizations is costly, as such associations would have to be collected from humans for a large variety of concepts. To address the challenge of collecting this data, we introduce a method to extract color-concept associations automatically from a set of concept images. While the state-of-the-art method extracts associations from data with supervised learning, we developed a self-supervised method based on colorization that does not require the preparation of ground truth color-concept associations. Our key insight is that a set of images of a concept should be sufficient for learning color-concept associations, since humans also learn to associate colors to concepts mainly from past visual input. Thus, we propose to use an automatic colorization method to extract statistical models of the color-concept associations that appear in concept images. Specifically, we take a colorization model pre-trained on ImageNet and fine-tune it on the set of images associated with a given concept, to predict pixel-wise probability distributions in Lab color space for the images. Then, we convert the predicted probability distributions into color ratings for a given color library and aggregate them for all the images of a concept to obtain the final color-concept associations. We evaluate our method using four different evaluation metrics and via a user study. Experiments show that, although the state-of-the-art method based on supervised learning with user-provided ratings is more effective at capturing relative associations, our self-supervised method obtains overall better results according to metrics like Earth Mover's Distance (EMD) and Entropy Differenc

关键词： data mining

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Cardiovascular Disease Prediction Using Machine Learning Classifiers 9

Cardiovascular Disease Prediction Using Machine Learning Cla...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Ramu, M. Harshitha, Madduru Hanisha, Ravuru Chandana, Pujari Meghana, P. SVEC Computer Science and Systems Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350397376

cardiovascular illnesses are believed to be the major cause of death on a global scale. According to WHO data, cardiac illnesses are thought to be the cause of 30% of fatalities in developing nations. One in in four persons will get heart disease, especially as they get older. Machine learning classifiers have been used to predict the risk of cardiovascular diseases by analyzing a variety of clinical, demographic, and lifestyle data. By training on large, diverse datasets, these classifiers can identify patterns and relationships between different variables and accurately predict CVD risk in individuals. In the proposed study we have utilized the kaggle and ieee data port datasets for estimating the chances of heart disease. A model was developed using seven machine learning classifiers such as Random Forest, Logistic Regression, CART, KNN, Naive Bayes, Support vector machine (SVM), and Ada Boost. A variety of evaluation metrics can be used to evaluate the performance of the model. We have included accuracy, precision, sensitivity, specificity, as well as ROC, F1 score, Mathew correlation and log loss. The accuracies of the model when treated with each classifier were 94%, 78%, 91%, 77%, 75%, 76%, and 78% respectively. The data visualization was created to show how the features relate to one another. The results of the studies show that the random forest method validates 2213 data instances using 12 attributes with 94% accuracy. © 2023 ieee.

关键词： Logistic regression

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Skeleton Based Keyframe Detection Framework for Sports Action Analysis: Badminton Smash Case

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ieee ACCESS 2023年 11卷 90891-90900页

作者： Sarwar, Muhammad Atif Lin, Yu-Chen Daraghmi, Yousef-Awwad Ik, Tsi-Ui Li, Yih-Lang Natl Yang Ming Chiao Tung Univ Coll Comp Sci Dept Comp Sci Hsinchu 30010 Taiwan Palestine Tech Univ Kadoorie Dept Comp Syst Engn Tulkarm 310 Palestine

The analysis of badminton player actions from videos plays a crucial role in improving athletes' performance and generating statistical insights. The complexity and speed of badminton movements pose unique challenges compared to everyday activities. To analyze badminton player actions, we propose a skeleton-based keyframe detection framework for action analysis. Keyframe detection is widely used in video summarization and visual localization due to its computational efficiency and memory optimization compared to analyzing all frames of a video. This framework segments the complex macro-level activity into micro-level segments and analyzes each micro-level activity individually. Firstly, it extracts skeleton data from a motion sequence video using 3D:VIBE pose estimation. Then, the keyframe detection module explores the sequence of activity frames and identifies keyframes for each micro-level activity, including start, ready, strike, and end. Finally, the posture and movement detection modules analyze the posture and movement data to identify specific activities. This framework is implemented in the device called CoachBox. The proposed framework is evaluated using the mean absolute error on a dataset. The average mean absolute error for the keyframe detection module is less than 0.168 seconds, and the striking moment detection has an error of only 0.033 seconds. Additionally, a coordinate transform method is provided to convert body coordinates to real-world coordinates for visualization purposes.

关键词： Videos Skeleton Three-dimensional displays Sports Cameras Feature extraction data mining Keyframe detection action analysis skeleton detection coordinate transform action analysis framework

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Semantic Navigation of PowerPoint-Based Lecture Video for AutoNote Generation

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ieee TRANSACTIONS ON LEARNING TECHNOLOGIES 2023年第1期16卷 1-17页

作者： Xu, Chengpei Jia, Wenjing Wang, Ruomei He, Xiangjian Zhao, Baoquan Zhang, Yuanfang Univ New South Wales Sch Minerals & Energy Resources Engn MIoT & IPIN Lab Sydney NSW 2052 Australia Univ Technol Sydney Sch Elect & Data Engn Sydney NSW 2007 Australia Sun Yat Sen Univ Natl Engn Res Ctr Digital Life Guangzhou 510275 Peoples R China Univ Nottingham Ningbo China Sch Comp Sci Ningbo 315104 Peoples R China

With the increasing popularity of open educational resources in the past few decades, more and more users watch online videos to gain knowledge. However, most educational videos only provide monotonous navigation tools and lack elaborating annotations. This makes the task of locating interesting contents time consuming. To address this limitation, in this article, we propose a slide-based video navigation tool that is able to extract the hierarchical structure and semantic relationship of visual entities in videos, by integrating multichannel information. Features of visual entities are first extracted from the presentation slides by a novel deep learning framework. Then, we propose a clustering approach to extract hierarchical relationships between visual entities (e.g., formulas, texts, or graphs appearing in educational slides). We use this information to associate visual entities with their corresponding audio speech text, by evaluating their semantic relationship. We present two cases where we use the structured data produced by this tool to generate a multilevel table of contents and notes to provide additional navigation materials for learning. The evaluation experiments demonstrate the effectiveness of our proposed solutions for visual entity extraction, hierarchical relationship extraction, as well as corresponding speech text matching. The user study also shows promising improvement in the autogenerated table of contents and notes for facilitating learning.

关键词： visualization Navigation Feature extraction Semantics data mining Deep learning Layout AutoNote generation educational videos hierarchical relationship extraction video navigation visual entity segmentation

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An Optimized 3D Tile Scheduling Method Adapted to Terminal's GPU DRAM 5

An Optimized 3D Tile Scheduling Method Adapted to Terminal's...

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5th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2023

作者： Shang, Xiaoyun Jia, Qingren Huang, Yazhe Yang, Quan Chen, Juan College of Electronic Science and Technology National University of Defense Technology Changsha China Tianjin Institute of Advanced Technology Tianjin China

ISBN: (纸本)9798350329551

3D rendering and visualization have become integral components of numerous applications. However, rendering large-scale oblique photogrammetry models on terminals with varying GPU capabilities poses significant challenges. One of the primary issues is the efficient allocation of GPU DRAM to handle the rendering workload. Traditional methods often struggle to balance memory consumption, leading to performance bottlenecks and inefficient resource utilization. In this paper, we propose a novel approach to handle this challenge. Our method focuses on optimized 3D tile scheduling, specifically designed to adapt the GPU DRAM capacities of individual terminals. By canceling tile requests that exceed the maximum rendering memory allocation, we prevent unnecessary accumulation of the tiles within the cache, thereby maintains the valuable memory resources. The experimental results show that the proposed method performs well in saving memory consumption and visualizing large-scale photogrammetry 3D models across different terminal capabilities. © 2023 ieee.

关键词： Graphics processing unit

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