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Utilization of Augmented Reality Technology in Campus Promotion Using the Marker Based Tracking Method

Utilization of Augmented Reality Technology in Campus Promot...

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International conference on Informatics and computing (ICIC)

作者： Heny Pratiwi Muhammad Ibnu Sa’Ad Oktadius Giantoro STMIK Widya Cipta Dharma Information System Samarinda Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This study aims to develop an Augmented Reality (AR) application as a more attractive and interactive promotional media for the STMIK Widya Cipta Dharma campus. By utilizing AR technology, it is hoped that information about facilities, study programs, and campus activities can be presented in a more attractive and easily understood way by prospective students and the general public. In addition to using conventional promotional methods such as social media and print media (brochures, posters, and billboards), the application of AR aims to enrich and increase the effectiveness of campus promotions. The methods used in this study include the development of an Android-based AR application with Canva software for interface design, Unity 2022 as the main program, and SketchUp for 3D modeling. The application development process follows a multimedia development model consisting of six stages: Concept, Design, Material Collection, Assembly, Testing, and Distribution. System testing is carried out using the Black Box and Beta Testing methods to ensure the application functions as expected. The results of this study indicate that the Implementation of Augmented Reality as a Promotional Media for the STMIK Widya Cipta Dharma Campus can be successfully completed using Unity 2022. This application is then distributed to the public who need information about STMIK Widya Cipta Dharma.

关键词： System testing Three-dimensional displays Social networking (online) Operating systems Two-dimensional displays Media User interfaces Three-dimensional printing Augmented reality Assembly

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Energy And Congestion Awareness Traffic Scheduling In Hybrid Software-Defined Network

Energy And Congestion Awareness Traffic Scheduling In Hybrid...

引用

computing (ICOCO), IEEE International conference on

作者： C.S. Lim S.C. Tan N. Q. Baderulhisham Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9781665489973

The term “Hybrid Software-Defined Network” (HSDN) refers to a network that includes both traditional networking devices and Software Defined networking (SDN) devices. The segregation of the control and data planes in SDN tools that enables centralized control of SDN traffic. Traffic scheduling in H-SDN can be handled through the centralized controller to alleviate traffic congestion. Traffic scheduling that includes flow splitting provides flexibility for traffic flow in order to reduce overload on the network. The network’s energy efficiency is critical for cost and energy savings. As a result, the energy consumption of a traffic flow should be considered during traffic scheduling. As a result, the goals of this paper are twofold: First, identify the research challenges in hybrid SDN energy and congestion traffic scheduling. Second, we will discuss future research directions for energy and congested-aware traffic scheduling in Hybrid SDN.

关键词： Energy consumption Costs Processor scheduling Energy efficiency Software defined networking Traffic congestion Centralized control

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M2SN: Adaptive and Dynamic Multi-modal Shortcut Network Architecture for Latency-Aware Applications

M2SN: Adaptive and Dynamic Multi-modal Shortcut Network Arch...

引用

IEEE International conference on multimedia and Expo (ICME)

作者： Yifei Pu Chi Wang Xiaofeng Hou Cheng Xu Jiacheng Liu Jing Wang Minyi Guo Chao Li Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Computer Science and Engineering Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Multi-modal neural networks have demonstrated exceptional performance by merging information across modalities, surpassing the state-of-the-art uni-modal DNNs. However, this accuracy improvement comes at the cost of increased computation, leading to higher inference latency. This defect significantly limits the practical value of multi-modal DNNs, especially for latency-aware applications. Therefore, we propose an adaptive and efficient multi-modal shortcut architecture called M 2 SN to reduce the execution latency with accuracy guarantees. It skips ineffective network layers to reduce computational costs as well as alleviate the overfitting problem adaptive to specific models and scenarios. The key contributions of M 2 SN are twofold: 1) We design and insert shortcuts into each uni-modal network to perform adaptive computing. 2) We design a navigator to dynamically choose the optimal shortcuts. Unlike previous approaches, M 2 SN features high generality as it does not rely on any prior knowledge. The experimental results show that M 2 SN can reduce 28.3% average latency while obtaining the same or higher accuracy compared with SOTA baselines.

关键词： Accuracy Adaptive systems Navigation Neural networks Merging Computer architecture Network architecture Explosions Robots Next generation networking

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Alzheimer’s Disease Classification via Pre-trained Convolutional Neural Network Variants Utilizing Multi-level Thresholding based on Kapur’s Entropy

Alzheimer’s Disease Classification via Pre-trained Convolut...

引用

Network, multimedia and Information Technology (NMITCON) International conference on

作者： Nivedhitha Mahendran P M Durai Raj Vincent Department of Networking and Communications School of Computing Technologies SRM Institute of Science and Technology Chennai Kattankulathur India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Neurological diseases are the disorders of brain and the nerves that run throughout the human body. There is a range of symptoms that arise as a result of chemical, structural, or electrical modifications in the regions of brain. One such neurological disease is the Alzheimer’s Disease (AD), is considered as a slow and progressive disease that majorly affects the cognitive abilities of the individual. It is most common forms of dementia without any proper cure. However, early diagnosis and treatment might slow down the symptoms and improve the lifestyle of the individual. The most common techniques to identify the neurological disorders are imaging of the brain regions, namely, CT, MRI, PET, etc. The learning approaches, such as Deep Learning and Machine Learning are identified to be effective in analyzing these images to provide better accuracies in detecting the AD. In this study, four versions of Convolutional Neural Networks (CNN) are used to classify AD and Control cases. Further, the Kapur’s thresholding technique is also implemented to segment the images for improving the classification accuracy of AD. From the results shown in the study, the inference shows that the VGG16 of CNN provides considerable improvement than the other deep learning models before and after the implementation of thresholding.

关键词： Deep learning Neurological diseases Image segmentation Accuracy Thresholding (Imaging) Magnetic resonance imaging Computed tomography Convolutional neural networks Alzheimer's disease Information technology

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Identification of Misogyny on Social Media in Indonesian Using Bidirectional Encoder Representations From Transformers (BERT)

Identification of Misogyny on Social Media in Indonesian Usi...

引用

International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Bagas Tri Wibowo Dade Nurjanah Hani Nurrahmi School of Computing Telkom University Bandung Indonesia

Misogyny is a behavior that hates or dislikes women Text classification can be used to identify misogyny text. One text classification method currently popular and proven to have good performance is the Bidirectional Encoder From Transformers (BERT). Fine-tuning is a method to transfer knowledge from a trained model to a new model to complete a new task. This study focuses on building a misogyny identification model with IndoBert pre-trained model provided by IndoNLU. The identification of Misogyny model obtained the best results with an accuracy value of 83.74% and by using K-fold cross-validation, the average validation value is 77.86%.

关键词： Video on demand Social networking (online) Bit error rate Text categorization multimedia Web sites Hate speech Buildings

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Text Emotion Analysis Using Deep Learning For COVID- 19 Dataset

Text Emotion Analysis Using Deep Learning For COVID- 19 Data...

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Advances in computing, Communication Control and networking (ICAC3N), International conference on

作者： Riya Garg Parjanya Aditya Shukla Umang Rathore Computer Science Engineering Galgotias College of Engineering and Technology Greater Noida India

Managing mental health and psychological well-being is just as critical as managing physical health throughout COVID-19. The difficulty of detecting, classifying, and quantifying emotions in text in any form are addressed in this study. We consider English text collected from social media sites such as Twitter and various Kaggle datasets that can provide information useful in a variety of ways, particularly opinion mining. However, analysing and categorising text based on emotions is a difficult task and might be thought of as a more advanced kind of Sentiment Analysis. This work provides a system for categorising text into three types of emotions: positive, negative, and neutral. This analysis can be utilized by authorities to better understand people's mental health and to make appropriate policy decisions to combat the coronavirus, which is hurting the world's social well-being and economy.

关键词： COVID-19 Deep learning Sentiment analysis Social networking (online) Semantics Blogs multimedia Web sites

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Develop Drama Performances Featuring Virtual Characters for Utilization in Language Learning

Develop Drama Performances Featuring Virtual Characters for ...

引用

International conference on Applied System Innovation (ICASI)

作者： Yi-Chen Lai Hong-Yi Pai Department of Multimedia Design National Formosa University Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

As VTubers rapidly grow, learning through virtual character role-play in language learning can integrate more deeply into educational contexts, offering a more effective alternative to traditional drama performances. To develop a more effortless and convenient system for multiplayer online virtual character role-play, this research aims to facilitate ubiquitous learning. This study has designed a virtual role-playing-based language conversation learning method and developed a Multiplayer Online Virtual Drama System (MOVDS). This system features four key functionalities: 1) a Cloud Data Module, 2) a Motion Capture Module, 3) Voice Recognition, and 4) a Synchronization Module with networking. The system provides real-time role-playing capabilities by synchronizing the facial expressions of virtual characters with live actors. It realizes the creative presentation of dramatic scenarios through voice chat, text prompts, and animations. This innovative approach boosts performers' confidence and reduces the learners' anxiety and cognitive load associated with spoken expression, thereby enhancing users' foreign language conversation skills.

关键词： Learning systems Humanities Cloud computing Technological innovation Oral communication Speech recognition User experience

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Cyber Security for Hoax News Detection with Similarity Algorithm

Cyber Security for Hoax News Detection with Similarity Algor...

引用

International conference on Informatics and computing (ICIC)

作者： Sy.Yuliani Yakub David Agustriawan Irmawati Dinar Ajeng Kristiyanti Shahrin Sahib Mohd Faizal Abdollah Mohammed Nasser Al-Mhiqani Yusuf Durachman The Informatic Study Program Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Faculty of Information Communication Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Department of Cyber Security University of Huddersfield United Kingdom Department of Information System State Islamic University of Syarif Hidayatullah Jakarta Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Hoax news is false information disseminated to deceive or mislead audiences, often with the aim of swaying opinions or creating confusion. The rise of social media has amplified the spread of hoax news, particularly in sensitive areas such as politics and health. In response to this growing issue, this paper proposes a Natural Language Processing (NLP) approach of detecting hoax news using the Smith-Waterman similarity algorithm. By comparing news content with a curated dataset of verified hoaxes, the system calculates a similarity score to assess the likelihood of the news being false. The results of this study show that news articles analyzed using the Smith-Waterman algorithm achieve a high accuracy, with a similarity score exceeding $93 \%$ for news inputs over 100 words. Furthermore, the proposed system demonstrates an efficient processing time, completing the analysis in approximately 6.57 seconds. These findings underscore the algorithm’s potential for real-time application in detecting fake news on social media and other digital platforms. This research aims not only to enhance the technical capabilities of hoax detection systems but also to foster greater media literacy and a more informed public.

关键词： Accuracy Social networking (online) Scalability Approximation algorithms Real-time systems Natural language processing Fake news Informatics Optimization Testing

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Unveiling Sentiment Dynamics: Emotion Detection in Social Media

Unveiling Sentiment Dynamics: Emotion Detection in Social Me...

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Intelligent computing and Emerging Communication Technologies (ICEC), International conference on

作者： Bhakti Pithava Abhay Magar Santosh Bharti Department of CSE SOT Pandit Deendayal Energy University Gandhinagar Gujarat India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508449

The paper demonstrate the interesting task of detecting emotions within the scope of natural language processing. Since many people have resorted to social media applications such as Facebook, Instagram, and others to express their ideas and emotions, it has become inevitable to address the issue of emotion based classification and interpretation of a given text. Also, in contrast to sentiment where one would only assess if a certain text is positive or negative, emotional detection goes a step further where it focuses on certain aspect of emotions. For this purpose, the authors make use of a range of machine learning and natural language processing capabilities. However, as content on the internet keeps increasing, so is the trend in concern with emotion detection which goes beyond simple sentiment analysis. In this study, although emotions can be understood in different forms, such as through speech, facial expressions the main concern is only about the reading text emotional understanding. The paper highlights the progress from simplistic sentiment analysis to complex emotion detection, outlining challenges as well as recapping the progress made in the area in recent times. It looks into the processes used to classify emotions including their extensive categories and the commonsensical model. In the course of time, aided by the improvements in machine learning and deep learning emotions contained in a piece of writing can be identified and evaluated, further evolved from the traditional concepts of keywords and key phrases, to the more advanced approaches which are able to construct the emotions of human beings.

关键词： Deep learning Emotion recognition Sentiment analysis Social networking (online) Blogs Writing Market research Communications technology Web sites multimedia communication

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Late Fusion of Text and Images for Predicting User Interests in Pinterest Social Network

Late Fusion of Text and Images for Predicting User Interests...

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International conference on Electrical Engineering, computing Science and Automatic Control (CCE)

作者： Areli Cabrera Oros Jonathán de Jesús Estrella Ramírez Juan Carlos Gómez Carranza División de Ingenierías Campus Irapuato-Salamanca Universidad de Guanajuato Salamanca Gto. México

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

Due to the large amount of information on social media, predicting user interest becomes crucial for various tasks including the improvement of the user experience in these platforms. This paper introduces a late fusion model designed to predict one of 29 predefined categories (considered as the user interests) established by Pinterest for any new pin uploaded by a user. A pin is an information unit with two components: an image and a short text comment. Given a new pin, the proposed model performs a multimodal classification by transforming and classifying each data modality (image and text) independently and then fusing the category probabilities with a weighted sum to predict the final categories for the pin. To test the validity of the fusion, a set of experiments is conducted analyzing and comparing the performance of different state-of-the-art deep learning transformations for each modality; and using a logistic regression model for classification. Additionally, the fusion is evaluated under different values for a weighting factor, in charge of assigning less or more importance to each data modality in the final decision. The experiments are carried out over a dataset composed of 29,000 pins collected directly from the Pinterest website. The results show that the late fusion of image and text representations increases the accuracy of predicting user interests when compared to the use of each single modality.

关键词： Deep learning Logistic regression Accuracy Social networking (online) Transforms Predictive models Vectors Pins Web sites multimedia communication

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