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Applying human computation mechanisms to Information Retrieval

Proceedings of the ASIST Annual Meeting 2012年第1期49卷 1-10页

作者： Harris, Christopher G. Srinivasan, Padmini Informatics Program University of Iowa Iowa City IA 52242 United States Computer Science Department and Informatics Program University of Iowa Iowa City IA 52242 United States

Crowd sourcing and Games with a Purpose (GWAP) have each received considerable attention in recent years. These two human computation mechanisms assist with tasks that cannot be solved by computers alone. Despite this increased attention, much of this transformation has been limited to a few aspects of Information Retrieval (IR). In this paper, we examine these two mechanisms' applicability to IR. Using an IR model, we apply criteria to determine the suitability of these crowd sourcing and GWAP mechanisms to each step of the model. Our analysis illustrates that these mechanisms can apply to several of these steps with good returns.

关键词： Crowd sourcing Games with a purpose GWAP Human computation Information Retrieval

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Automatic Creation of Quality Images from Text using Multiple Generative Adversial Network 3

Automatic Creation of Quality Images from Text using Multipl...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Devasana, M.S. Geetha Kumar, V. Krishna Selvaraj, R. Satheesh, T. Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore641022 India Department of Computer Science and Engineering Dr.N.G.P Institute of Technology Coimbatore641048 India Department of Artificial Intelligence and Data Science Dr.N.G.P Institute of Technology Coimbatore641048 India

Automatic creation of realistic images is a tedious process even though the state-of-the-art AI/ML algorithms are employed. There is a lot of demand for such automatic image generators that could create high quality images. Many have this problem of visualizing things from the explanations they hear about. Thus, the text to image generation problem is necessary because it has significant applications in CAD, art generation and many more. This is a challenging task since the image should be realistic and consistent with the text. One of the most common uses of modern conditional generative models is the generation of visuals from natural language. Viewing an image makes us easily understand what that image is, rather than hearing someone describing the image. To bridge the semantic gap between text and image, Generative Adversarial Network (GAN) systems are used to achieve high accuracy. The proposed system helps in generation of superior quality images that are meaningfully consistent with the text. © 2023 Elsevier B.V.. All rights reserved.

关键词： Generative adversarial networks

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Tropical Cyclone Detection and Tracking Using YOLOv8 Algorithm 1

Tropical Cyclone Detection and Tracking Using YOLOv8 Algorit...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Rout, Saroja Kumar Kumar, Kottu Santosh Sahu, Ruchismita Barik, Shekharesh Pradhan, Samarendra Department of Information Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Hyderabad India Templecity Institute of Technology and Engineering Department of Computer Science & Engineering Bhubaneswar India DRIEMS University Department of Computer Science & Engineering Cuttack India Data scientist Jayapur Cuttack India

ISBN: (纸本)9798350361780

Coastal areas around the world face a great threat from tropical cyclones, which makes timely and accurate identification essential for efficient disaster response. An enhanced method for detecting tropical cyclones is proposed in this work using the YOLOv8 (You Only Look Once version 8) object identification algorithm. Using the real-time object recognition capabilities of YOLOv8, the methodology is designed to tackle the challenges associated with finding cyclonic structures in satellite data. To detect crucial features typical of tropical cyclones - like cloud formations, circular symmetry, and dynamic atmospheric fluctuations - the YOLOv8 architecture is improved. To ensure its adaptability to different climatic circumstances, the model is trained using a varied dataset of labeled satellite photos. The YOLOv8-based methodology is superior to existing methods in detecting tropical cyclones with accuracy and efficiency almost in real-time, as demonstrated by the experimental results. Because of the algorithm's excellent precision, recall, and computing economy, early warning systems and catastrophe preparedness can benefit greatly from it. This research contributes to the ongoing effort to develop detection approaches for increased resilience in vulnerable coastal areas, while also utilizing YOLOv8's capabilities to address the global threat posed by tropical cyclones. This study can help clarify the necessity of improving models and conducting better observations of Tropical Cyclones. © 2024 IEEE.

关键词： Object detection

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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Multi-StudentNet: a Semi-supervised Deep-Learning Framework for Endometrial Segmentation in Transvaginal Ultrasound Images

Multi-StudentNet: a Semi-supervised Deep-Learning Framework ...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Peng, Boyuan Liu, Yiyang Zhu, Xin Huang, Jie Wang, Wenwen Fang, Li Zhou, Qin Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Chiyodaku Tokyo Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350350548

Semi-supervised learning (SSL) is a promising solution for the problem of insufficient medical labeled data. However, it is still a challenging task to segment transvaginal ultrasound (TVUS) images because of their poor contrast and speckle noise. In this study, we propose Multi-StudentNet, a semi-supervised deep-learning framework for endometrial segmentation in TVUS images. This method employs multiple student models to effectively integrate labeled and unlabeled data, improving the reliability and performance of segmentation while significantly reducing the need for extensive manual labeling. By leveraging the weights of multiple models, our framework facilitates feature sharing, which enhances model robustness. Specifically, Multi-StudentNet achieves a Dice coefficient (DSC) of 0.81 and a specificity of 0.99. Our multi-student models consistently outperform single-student models across various conditions, including normal, polyp, and cancer cases. Extensive experiments demonstrate that Multi-StudentNet achieves state-of-the-art performance in both accuracy and robustness. © 2024 IEEE.

关键词： Self-supervised learning

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A Model to Enhance Governance Issues through Opinion Extraction 11

A Model to Enhance Governance Issues through Opinion Extract...

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11th Annual IEEE Information Technology, Electronics and Mobile Communication Conference, IEMCON 2020

作者： Shaukat, Kamran Alam, Talha Mahboob Ahmed, Muhammad Luo, Suhuai Hameed, Ibrahim A. Iqbal, Muhammad Shahid Li, Jiaming Iqbal, Muhammad Atif School of Electrical Engineering and Computing University of Newcastle Newcastle Australia Department of Computer Science University of Engineering and Technology Lahore Pakistan Department of Ict and Natural Sciences Norwegian University of Science and Technology Norway Department of Computer Science Air University Islamabad Pakistan Data61 Commonwealth Scientific and Industrial Research Organization Australia

ISBN: (纸本)9781728184166

We live in a world where data is expanding exponentially. Most of the data is unstructured when obtained through the web. Many organizations, institutes, and governments worldwide gather public views regarding their products, services, or policies. With thousands of reviews about some product, service, or policy, it is impossible to conclude some kind of final thought from it. To handle this, there is a desperate need for a model that can extract meaningful information from data to make correct and timely decisions for the efficient growth of business and smooth running of an organization or government. Otherwise, the practice of collecting and storing data will be ineffective. In this study, we focused on conducting an extensive public survey on issues of Southern Punjab, carry out appropriate processing on collected data and predict trends in public opinion for decision-making. Natural Language Processing (NLP) and Machine Learning (ML) have dealt with this problem. Different data preprocessing techniques have been utilized to remove the noise from data. Our experiments stated that unemployment, poverty, education, and corruption are the major issues of the targeted region. This study will help government officials and non-governmental organizations to be focused on the extracted issues in the specific region. © 2020 IEEE.

关键词： Sentiment analysis

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Comparing CUDA, OpenCL and OpenGL implementations of the cardiac monodomain equations

Comparing CUDA, OpenCL and OpenGL implementations of the car...

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9th International Conference on Parallel Processing and Applied Mathematics, PPAM 2011

作者： Sachetto Oliveira, Rafael Rocha, Bernardo Martins Amorim, Ronan Mendonça Campos, Fernando Otaviano Meira Jr., Wagner Toledo, Elson Magalhães Dos Santos, Rodrigo Weber Federal University of São João del Rey Department of Computer Science Brazil Federal University of Minas Gerais Department of Computer Science Brazil Federal University of Juiz de Fora Department of Computer Science Computational Modeling Program Brazil National Laboratory of Scientific Computing Brazil Medical University of Graz Institute of Biophysics Austria

ISBN: (纸本)9783642314995

computer simulations of cardiac electrophysiology are a helpful tool in the study of bioelectric activity of the heart. The cardiac monodomain model comprises a nonlinear system of partial differential equations and its numerical solution represents a very intensive computational task due to the required fine spatial and temporal resolution. Recent studies have shown that the use of GPU as a general purpose processor can greatly improve the performance of simulations. The aim of this work is to study the performance of different GPU programming interfaces for the solution of the cardiac monodomain equations. Three different GPU implementations are compared, OpenGL, NVIDIA CUDA and OpenCL, to a CPU multicore implementation that uses OpenMP. The OpenGL approach showed to be the fastest with a speedup of 446 (compared to the multicore implementation) for the solution of the nonlinear system of ordinary differential equations (ODEs) associated to the solution of the cardiac model, whereas CUDA was the fastest for the numerical solution of the parabolic partial differential equation with a speedup of 8. Although OpenCL provides code portability between different accelerators, the OpenCL version was slower for the solution of the parabolic equation and as fast as CUDA for the solution of the system of ODEs, showing to be a portable way of programming scientific applications but not as efficient as CUDA when running on Nvidia GPUs. © 2012 Springer-Verlag.

关键词： Nonlinear systems

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database management system design for COVID-19 symptom analysis 6

Database management system design for COVID-19 symptom analy...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Rahutomo, Reza Darmalim, Ferdinan Cen, Andrew Brilianto, Jayaku Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia PT. Mega Medica Pharmaceuticals IT Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Faculty of Technology and Design Bunda Mulia University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Applying a digital strategy is proven to be one way to reduce the infection rate of COVID-19. As a fundamental part of it, database is capable to create a data repository to predict uncertain impacts of the pandemic. The research proposed a database design for project C-Beta that emphasized on creating an ideal system to record and monitor the daily health condition of people with COVID-19. Mainly, the research adopted Connolly and Begg principles of database Software Development Life Cycle. As a result, the database design activities produced blueprints in the forms of data dictionary, ERD, and user views. The database design delivered in the research included with several features namely user verification, restricted research membership, and flexibility in survey creation. Based on analysis and design phase, C-Beta provides a method to collect data for COVID-19 clinical study. Researchers who utilize C-Beta is able to manage multiple surveys and extract all collected records at the same time. © 2021 IEEE.

关键词： COVID-19

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Enhanced Detection and Analysis of Lymphoma Cancer Through Machine Learning and Deep Learning 1

Enhanced Detection and Analysis of Lymphoma Cancer Through M...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Choudhary, Doddapaneni Meghana Ashwini, Bugata Sai Rasagna Devi, Kavuluri Leela Kedareswari, Kotte Kiran, Musunuru Hari Srinivas, Pvvs Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology Andhra Pradesh Vaddeswaram522502 India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh Vaddeswaram522502 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350367720

This study focuses on the use of ml techniques to unravel the complexity of lymph node classification, which is an important part of oncology due to its different subtypes and different treatments. This study aims to develop an accurate classification model for different types of lymphoma using image datasets in the "TRAIN_DIR"and "TEST_DIR"maps. The research combines methods such as support vector machine (SVM), decision trees, logistic regression, and naive Bayes and performs rigorous and effective training methods to optimize the performance of models. Advanced preprocessing methods and data augmentation techniques are used to improve the model's ability to detect subtle changes in biopsy images, ultimately aiming to increase diagnostic accuracy and support self-healing strategies. This approach has the potential to improve clinical decision-making and patient care in lymphoma management. Diagnose and fix problems. The study combined machine learning algorithms to analyze biopsy images to accurately classify lymphoma subtypes. The training model focuses on improving the sensitivity and specificity of subtype detection, which is important for creating an effective patient-based treatment plan. Using the computational power of support vector machines, decision trees, logistic regression, and naive Bayes, this research can help improve oncology diagnostic tools, reducing diagnostic uncertainty and supporting more treatments. The aim of this research is to advance the work of personalized medicine by creating a powerful computational model that can reliably distinguish between lymphoma subtypes. By automating and improving the accuracy of subtype classification through machine learning, the research aims to streamline diagnostic workflows and help make timely diagnostic decisions. © 2024 IEEE.

关键词： Biopsy

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"Nyctophy": Assessing the effectiveness of serious game for nyctophobia based on virtual reality by usability and heart rate monitoring

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Procedia computer science 2024年 245卷 185-192页

作者： Elshad Ryan Ardiyanto Patrick Alexander Edyth Novian Putra Dimas Ramdhan Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta

Nyctophobia is a phobia of the dark and is common among children but also found in adults. While the phobia itself is commonly known, the diversity of its treatment is still minimal. As technology has reached its highest point, numerous inventions and innovations have emerged in the medical sector. This study aimed to examine the usability of Virtual Reality and Keyboard and Mouse as an interface using the Extended Reality Game Usability Questionnaire (XR-GUQ) in Nyctophobia therapy and to analyze the heart rate response, impact, and implications using t-test Paired/Wilcoxon Signed Rank. Thirty-four Bina Nusantara University students from various backgrounds completed the test using interfaces and a smartwatch. They were divided into two groups, high- and low fear, using the modified Acrophobia Questionnaire (AQ), which resulted in a significant difference. The XR-GUQ result was insignificant in all variables, which indicates similar usability scores on both interfaces. The t-test Paired/Wilcoxon Signed Rank test revealed a significant difference between the high- and low-fear groups when using the Virtual Reality interface compared to the Keyboard and Mouse interface. This significant difference was found within each group regardless of the interface used. This research shows similar scores in terms of usability but a significant difference in heart rate response with the help of a serious game to give a new sensation of phobia therapy.

关键词： Nyctophobia Virtual Reality Keyboard and Mouse Usability Heart Rate Serious Game Effectiveness

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