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International Conference on computer and Information Technology (ICCIT)

作者： Mahmudul Hasan Sara Jerin Prithila Tamim Al Ahasan Mahmudul Hassan Abid Hossain Sania Azhmee Bhuiyan Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

Image captioning means generating relevant texts from images that describe the image. With the growing advancement of deep learning, automatic image caption generation has become an exciting problem among researchers. Image captioning is used in various fields of computer science including computer vision and NLP. It is helpful in many cases such as visual impairment. Though many research works have already been published on this topic using different deep learning models, we can work with various features of the deep learning models to achieve better accuracy. In this paper, we are focusing on combining several deep-learning models to get the desired accuracy. VGG16 model is used which is a CNN architecture to extract essential features from the image. For the generation of relevant captions, LSTM is adopted rather than RNNs as LSTM generates better captions compared to RNNs and it consumes less time. Finally, the model is trained on Flickr 8k datasets.

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Large Language Model (LLM)-enabled Graphs in Dynamic Networking

arXiv

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arXiv 2024年

作者： Sun, Geng Wang, Yixian Niyato, Dusit Wang, Jiacheng Wang, Xinying Poor, H. Vincent Letaief, Khaled B. The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Meanwhile, enhancing dynamic network performance is a crucial element in promoting technological advancement and meeting the growing demands of users in many applications areas involving networks. In this article, we explore an integration of LLMs and graphs in dynamic networks, focusing on potential applications and a practical study. Specifically, we first review essential technologies and applications of LLM-enabled graphs, followed by an exploration of their advantages in dynamic networking. Subsequently, we introduce and analyze LLM-enabled graphs and their applications in dynamic networks from the perspective of LLMs as different roles. On this basis, we propose a novel framework of LLM-enabled graphs for networking optimization, and then present a case study on UAV networking, concentrating on optimizing UAV trajectory and communication resource allocation to validate the effectiveness of the proposed framework. Finally, we outline several potential future extensions. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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2D Animation - Q Version of the AIDS Prevention Short Video 10

2D Animation - Q Version of the AIDS Prevention Short Video

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10th International Conference on Orange Technology, ICOT 2022

作者： Wei, Chun-Cheng Hsieh, Hui-Ming Hung, Long-Chen Chen, Ru-Yi Huang, Jiang-Fang Lu, Xin-Chun Zhong, Yu-Shan School of Software and Big Data Changzhou College of Information Technology Changzhou China Taiwan Shoufu University Department of Computer Science and Multimedia Design Tainan Taiwan Lunghwa University of Science and Technology Department of Electronic Engineering Taiwan

ISBN: (纸本)9781665454193

AIDS is a sexually transmitted disease. The medical term is called Acquired Immune Deficiency Syndrome. Medical methods such as cocktail therapy and anticancer drugs can kill AIDS. Although there are currently cured cases, prevention is still required to prevent the infection from expanding;prevention Ways, like the AIDS vaccine or preventive safe sex, can reduce the risk of infection. We hope that through the production of animated short films for viewing, we can help reduce the HIV infection rate. This short film is presented as a story similar to a micro-film by ourselves. And using the modern scene and the character design of the Q version as the material, the story content is made into 2D animation with Adobe Flash CS5. © 2022 IEEE.

关键词： Animation

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Impact of Application Choice on iTracker Model Performance Evaluation: A Perspective 2

Impact of Application Choice on iTracker Model Performance E...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Moghlebey, Adam Bou Haydar, Hady Moubayed, Abdallah Injadat, MohammadNoor School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Zarqa University Data Science and Artificial Intelligence Department Zarqa Jordan

ISBN: (纸本)9798350373363

Eye tracking is underpinned by the eye-mind hypothesis, which posits that individuals tend to direct their gaze toward the information they are currently cognitively engaged with. This aspect has garnered significant attention within deep learning research due to its versatile applications and research. The utilization of Artificial Intelligence (AI) in conjunction with webcams for tracking eye movements has gained increasing popularity in recent years and is anticipated to continue flourishing. This literature review aims to assess the current state and depth of the research in this domain and its various applications. We examine the different methods that employ eye tracking across a diverse range of applications, and by re-exploring existing research, we are able to filter out any overlooked potential or setbacks associated with the use of AI-webcam based systems, with the ultimate goal of maximizing their potential contributions to humanity. In our study, we analyze and evaluate a pioneering eye tracking algorithm, iTracker trained on a crowd-sourced data-set, GazeCapture within the context of the applications it may be used for. While we acknowledge inherent limitations, this widely adopted model forms a robust foundation for future refinements. Furthermore, our investigation encompasses diverse applications with varying accuracy requirements, making it invaluable for individuals seeking to integrate eye tracking into their projects. © 2023 IEEE.

关键词： Eye tracking

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DAC-MD: A privacy preserving drone-access control scheme for last mile delivery

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Transactions on Emerging Telecommunications Technologies 2024年第3期35卷

作者： Irshad, Azeem Farooq, Muhammad Mahmood, Khalid Mallah, Ghulam Ali Chaudhry, Shehzad Ashraf Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Mathematics and Statistics College of Arts and Sciences Abu Dhabi University Abu Dhabi United Arab Emirates Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliou Taiwan Department of Computer Science Shah Abdul Latif University Sindh Khairpur Pakistan Department of Computer Science and Information Technology College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Department of Software Engineering Faculty of Engineering and Architecture Nisantasi University Istanbul Turkey

The service delivery has lately witnessed strides in the form of paradigm shifts from conventional logistics to drone-oriented supply chain to conserve ecosystem. The use of drones or unmanned aerial vehicles (UAVs) for package deliveries offer enhanced accessibility, flexibility, efficiency, speed, safety, and even few environmental gains. However, the drones can come across various security hazards while communicating with ground installations on open channels including physical capture attack, privacy infringement, and so on. Thus, earnest endeavors are needed to fix those security challenges in applications related to drone communications. To address these problems, an efficient authenticated key agreement technique, drone access control scheme for last mile delivery (DAC-MD) is suggested for user-drone interactions in IoD environment that help the participating entities in developing a mutually agreed session key for securing subsequent communication interactions. The proposed scheme is proved using automated ProVerif tool as well as analyzed by using formal analysis based on RoR random oracle model. The comparative analysis against recently presented known studies reveals that our scheme not only carries comparable properties in terms of security and privacy, but also low computational and communication costs. © 2024 John Wiley & Sons, Ltd.

关键词： Drones

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An Experimental Study of Passive UAV Tracking with Digital Arrays and Cellular Downlink Signals

arXiv

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arXiv 2024年

作者： Sun, Yifei Yu, Chao Luo, Yan Han, Tony Xiao Tan, Haisheng Wang, Rui Lau, Francis C.M. The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong Huawei Technologies Company Ltd. Shenzhen518063 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230027 China

Given the prospects of the low-altitude economy (LAE) and the popularity of unmanned aerial vehicles (UAVs), there are increasing demands on monitoring flying objects at low altitude in wide urban areas. In this work, the widely deployed long-term evolution (LTE) base station (BS) is exploited to illuminate UAVs in bistatic trajectory tracking. Specifically, a passive sensing receiver with two digital antenna arrays is proposed and developed to capture both the line-of-sight (LoS) signal and the scattered signal off a target UAV. From their cross ambiguity function, the bistatic range, Doppler shift and angle-of-arrival (AoA) of the target UAV can be detected in a sequence of time slots. In order to address missed detections and false alarms of passive sensing, a multi-target tracking framework is adopted to track the trajectory of the target UAV. It is demonstrated by experiments that the proposed UAV tracking system can achieve a meter-level accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Long Term Evolution (LTE)

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WSpeller: Robust Word Segmentation for Enhancing Chinese Spelling Check

WSpeller: Robust Word Segmentation for Enhancing Chinese Spe...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Li, Fangfang Shan, Youran Duan, Junwen Mao, Xingliang Huang, MinLie School of Computer Science and Engineering Central South University China Institute of Big Data and Internet Innovation Hunan University of Technology and Business China Beijing National Research Center for Information Science and Technology Tsinghua University China

Chinese spelling check (CSC) detects and corrects spelling errors in Chinese texts. Previous approaches have combined character-level phonetic and graphic information, ignoring the importance of segment-level information. According to our pilot study, spelling errors are always associated with incorrect word segmentation. When appropriate word boundaries are provided, CSC performance is greatly enhanced. Based on these findings, we present WSpeller, a CSC model that takes into account word segmentation. A fundamental component of WSpeller is a W-MLM, which is trained by predicting visually and phonetically similar words. Through modification of the embedding layer's input, word segmentation information can be incorporated. Additionally, a robust module is trained to assist the W-MLM-based correction module by predicting the correct word segmentations from sentences containing spelling errors. We evaluate WSpeller on the widely used benchmark datasets SIGHAN13, SIGHAN14, and SIGHAN15. Our model is superior to state-of-the-art baselines on SIGHAN13 and SIGHAN15 and maintains equal performance on SIGHAN14. © 2022 Association for Computational Linguistics.

关键词： Errors

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Internet of Things Assisted Human Detection and Rescue Robot: A Game Changer in the Disaster Environment

Internet of Things Assisted Human Detection and Rescue Robot...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Md Jawed Akhtar Manmohan Singh Sajja Suneel Anand Sutar A. Periya Nayaki Nikhat Raza Khan Department of Computer Science & Engg IES College of Technology Bhopal Department of Computer Science and Engineering IES College of Technology Bhopal Mp India Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Dept of Electronics and Telecommunication Bharati Vidyapeeh College of Engineering Navi Mumbai Department of Information Technology Velammal College of Engineering and Technology Madurai Tamilnadu Department of Computer Science and Engineering IES College of Technology Bhopal

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In the realm of disaster response, the integration of Internet of Things (IoT) technologies has paved the way for innovative solutions to enhance human detection and rescue operations. This research work introduces a revolutionary approach through the development of the Challenging Human Detection and Rescue Robot (CHDRR). This robot incorporates advanced sensors, including the MQ6 Gas Sensor, LM335 Temperature Sensor, Humidity Sensor, and a Passive Infrared (PIR) Human Body Detection sensor, collectively forming a comprehensive sensory suite. The CHDRR leverages the power of IoT to enable real-time data collection, analysis, and communication, transforming disaster response capabilities. The MQ6 Gas Sensor ensures the detection of hazardous gases in the environment, providing crucial information for safe rescue operations. The LM335 Temperature Sensor and Humidity Sensor contribute to environmental monitoring, allowing the robot to adapt its strategies based on prevailing conditions. The PIR Human Body Detection sensor serves as a key element in the CHDRR's human detection capabilities. Through sophisticated algorithms and machine learning, the robot can identify and locate human presence amidst challenging disaster scenarios. The fusion of these sensors with IoT technology empowers the CHDRR to make informed decisions autonomously, optimizing its efficiency in dynamically changing environments.

关键词： Temperature sensors Disasters Humidity Sensor fusion Robot sensing systems Real-time systems Sensors

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Analyzing Public Sentiment on Social Media during FIFA World Cup 2022 using Deep Learning and Explainable AI

Analyzing Public Sentiment on Social Media during FIFA World...

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International Conference on computer and Information Technology (ICCIT)

作者： Shafakat Sowroar Arnob M. A. Ahad Shikder Tashfiq Alam Ovey Ehsanur Rahman Rhythm Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) BRAC University Dhaka Bangladesh

Analysis of public sentiment is extremely useful for comprehending the responses of the general public during important events, and the FIFA World Cup 2022 was no exception. Within the scope of this study, we used deep learning models such as roBERTa, distilBERT, and XLNet to conduct an analysis of the views that were stated on Twitter during the first day of the tournament. These models were fine-tuned using a comprehensive dataset consisting of 30,000 tweets, which had been preprocessed. The performance of these models was assessed using measures such as accuracy, F1-score, precision, recall, etc. In addition, we used an Explainable AI known as Local Interpretable Model-Agnostic Explanations (LIME) so that we could better understand how model decisions were made in sentiment classification. Our research has shown that roBERTa is an excellent model for classifying sentiment, and it has also shown the significance of interpretability achieved using LIME. Our research enhances the understanding of sentiment analysis during major sports events and suggests future directions for research in this domain.

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Adapting Contextual Embedding to Identify Sentiment of E-commerce Consumer Reviews with Addressing Class Imbalance Issues

Adapting Contextual Embedding to Identify Sentiment of E-com...

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Advances in Computing,Communication, Electrical, and Smart Systems (iCACCESS), International Conference on

作者： Md. Abdur Rakib Mollah Mir Md. Jahangir Kabir Md. Sazid Reza Monika Kabir Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Data Science and Innovation TD School University of Technology Sydney Sydney Australia Information Technology Murdoch University Perth Australia

Understanding consumer attitudes toward specific products is crucial for boosting sales in the e-commerce industry. To effectively target customers with popular products based on reviews, the classification of consumer feedback becomes imperative. However, classifying product reviews can be challenging, particularly when dealing with imbalanced data labels, which often result in suboptimal classification performance. This study builds upon previous efforts that utilized the Amazon Fine Food Reviews dataset for classification tasks. While these prior attempts showed promise, they were hindered by either poor embeddings or the prevalent class imbalance issue. In response, this research tries to solve these problems by using word embeddings with RoBERTa, a pre-trained transformer-based language model, to classify reviews. Additionally, the XGBoost classifier was implemented, along with embeddings from the language model. Losses were first calculated with equal weights for all class labels, and a re-weighted loss was subsequently adopted to balance the impact of each class on the loss function during training. The incorporation of RoBERTa and XGBoost, along with the class label re-weighting, contributed to improved capturing of intricate word relationships within reviews. As a result, this approach achieves significantly improved accuracy in both binary and multiclass classifications compared to earlier endeavors. Notably, it attained an impressive accuracy of 83.84% in multiclass classification and 93.29% in binary classification tasks, marking a substantial advancement in the field of consumer review analysis.

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