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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Davagdorj, Khishigsuren Park, Kwang Ho Amarbayasgalan, Tsatsral Munkhdalai, Lkhagvadorj Wang, Ling Li, Meijing Ryu, Keun Ho College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Department of Computer Science and Technology School of Computer Science Northeast Electric Power University Jilin City China College of Information Engineering Shanghai Maritime University 1550 Haigang Avenue Pudong New Area Shanghai213 China Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Biomedical Engineering Institute Chiang Mai University Chiang Mai50200 Thailand Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789811684296

The large volumes of biomedical documents have been generating exponentially in modern applications. Document clustering methods play an important role in gathering textual content documents into a few meaningful coherent groups. However, clustering unstructured and unlabeled text is challenging to extract informative representations and find the relevant articles from the rapid growth biomedical literature. Therefore, traditional text document clustering methods often represent unsatisfactory results due to general non-contextualized vector space representations, which neglect the semantic relation between bio medical texts. Pre-trained language models have been gaining attention recently in variety of natural language processing tasks. In this paper, we propose a heavily pre-trained language representation BioBERT based clustering framework for biomedical document analysis in order to improve the clustering accuracy. In experimental architecture, we provide benchmarks of the pre-trained transformer model, statistical technique and word-embedding methods while incorporating with clustering algorithms. In order to distinguish the efficiency of the models, Fowlkes mallows score (FM), silhouette coefficient (SC), adjusted rand index (ARI), Davies-Bouldin score (DB) metrics are used. The comprehensive experimental results show that the BioBERT based K-means model achieves better clustering accuracies than other models. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Automatic Smart Crawling on Twitter for Weather information in Indonesia

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Procedia computer science 2023年 227卷 795-804页

作者： Kartika Purwandari Reza Bayu Perdana Join W.C. Sigalingging Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta 10720 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Although Twitter is a popular platform for social interaction analysis and text data mining, it faces challenges with geolocation automation. To address this problem, the researchers propose the utilization of a Support Vector Machine (SVM) model to develop an automated Twitter crawling system. The system aims to collect data related to weather in Indonesia by employing Twin, a Python-based Twitter scraping software. To overcome null geolocations, the study incorporates aliases created based on the common practice of Indonesian users mentioning the country's location in tweets. The results demonstrate that the SVM model, combined with automated smart crawling, achieves an 85% accuracy rate.

关键词： Social media Twitter SVM smart crawling weather

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Quantized Communications in Target Enclosing of Autonomous Surface Vehicles with Prescribed Performance Control: Theory and Experiment

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Hao Peng, Yuhan Wang, Yueying Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China Northeastern University Foshan Graduate School of Innovation Foshan528311 China Tongji University School of Computer Science and Technology Shanghai201804 China

This paper investigates the problem of target enclosing control for autonomous surface vehicles (ASVs) that a follower tracking around a moving target. Specifically, the abilities of prescribed performance control (PPC) and quantized communications are injected into the target enclosing controller. Main features of the proposed algorithm are that: First, for individual tracking level, the target enclosing radius distance and angular errors are constrained within user-defined bounds, to guarantee these errors not escape the predefined regions. Second, for coordination control level, the communications between the ASVs are quantized, which significantly reducing the communication load and saving network bandwidth. Third, only the yaw rate is needed to complete the target enclosing mission, and do not need the kinetics information, which is an advantage for practical applications. Furthermore, Lyapunov stability theory is used to prove the boundedness of the system. Both theoretical analysis and experimental test are given to demonstrate the validity of the presented target enclosing control strategy. © 1967-2012 IEEE.

关键词： Bandwidth

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GAMIFICATION OF DISCUSSOO: AN ONLINE AI-BASED FORUM FOR SERIOUS DISCUSSIONS 16

GAMIFICATION OF DISCUSSOO: AN ONLINE AI-BASED FORUM FOR SERI...

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16th International Conference on Interfaces and Human computer Interaction, IHCI 2022, and 15th International Conference on Game and Entertainment Technologies 2022, GET 2022 - Held at the 16th Multi Conference on computer science and information Systems, MCCSIS 2022

作者： Bakhanova, Elena Anjum, Madiha Garcia, Jaime A. Raffe, William L. Voinov, Alexey UTS Games Studio Faculty of Engineering and Information Technology University of Technology Sydney 81-113 Broadway UltimoNSW2007 Australia School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney 81-113 Broadway UltimoNSW2007 Australia Faculty of Engineering Technology University of Twente Drienerlolaan 5 Enschede7522 NB Netherlands

ISBN: (纸本)9789898704405

Engagement in the discussion process is one of the common challenges of asynchronous online forums. It becomes especially crucial if the discussion is organized over a serious topic about a complex problem with a group of diverse stakeholders. Gamification gives much promise in addressing this challenge. In this paper, we propose possible game design solutions to the engagement challenge for an existing online AI-based platform Discussoo and reflect on the results from the expert interviews and an experiment with students. © 2022 International Conference on Interfaces and Human computer Interaction

关键词： Game design

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Real-time Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video in Compressed Domain

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APSIPA Transactions on Signal and information Processing 2023年第4期12卷

作者： Ardianto, Sandy Hang, Hsueh-Ming Cheng, Wen-Huang Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Our goal is to develop real-time vehicle detection and tracking schemes for fisheye traffic monitoring video using the temporal information in the compressed domain without decoding the entire video. Two algorithms are proposed. The first algorithm starts with a conventional single-frame detector, but we introduce a multi-frame information fusion stage to improve the final detection and tracking accuracy, which is implemented using multi-modal bi-directional LSTM (MM bi-LSTM) network. The second algorithm first constructs multi-frame motion trail image, and then a single-image multi-head detector is designed to produce bounding boxes of an individual frame. The first scheme can be viewed as a detect-to-track design, and the second scheme is track-to-detect. We tested our proposals on the ICIP2020 VIP Cup dataset in H.265 video format. The aforementioned algorithms are applied to the motion fields and residual images in the H.265 compressed data set. It turns out that their detection and tracking performances are on par with their pixel-domain counterparts, and they can achieve the state-of-the-art accuracy of conventional video object detectors and trackers. If the decoding process for video compression is not counted, their computational complexities are much lower than the conventional pixel-domain video object detectors and trackers. © 2023 S. Ardianto, H.-M. Hang and W.-H. Cheng.

关键词： Image compression

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Cooperative Autonomous Driving Control among Vehicles of Different Sizes Using Deep Reinforcement Learning

Cooperative Autonomous Driving Control among Vehicles of Dif...

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International Joint Conference on Neural Networks (IJCNN)

作者： Akito Takenaka Tomohiro Harada Yukiya Miura Kiyohiko Hattori Johei Matuoka Graduate School of Systems Design Tokyo Metropolitan University Tokyo Japan Graduate School of Science and Engineering Saitama University Saitama Japan Faculty of Systems Design Tokyo Metropolitan University Tokyo Japan Faculty of Computer Science Tokyo University of Technology Tokyo Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This study explores acquiring cooperative control in an environment where only multiple autonomous vehicles with different sizes navigate using deep reinforcement learning (DRL). Previous works proposed a cooperative learning approach utilizing information obtained through vehicle-to-vehicle communication. However, the previous research assumed all vehicles to be the same size. Indeed, in the real environment, vehicles of various sizes, such as compact cars and large trucks, coexist on the roads and must cooperatively drive. This study aims to achieve autonomous driving control among vehicles of different sizes using DRL and analyze the impact of vehicle size on cooperative behavior. We conducted simulation experiments using vehicles of sizes corresponding to regular cars and medium-sized trucks. Experimental results revealed that cooperative autonomous driving control was successfully acquired even in environments where vehicles of different sizes coexisted. Furthermore, the model trained exclusively with medium-sized trucks demonstrated the ability to mitigate collisions when operating even in environments with a mixture of vehicle sizes. In contrast, the model trained with a mixture of vehicle sizes demonstrated more collisions in the medium truck-only environment. This result indicates that a model trained in a challenging environment (where only large-sized vehicles exist) performs better in simpler environments (environments with only small-sized vehicles or a mix of small and large-sized vehicles) in terms of collision avoidance.

关键词： Training Navigation Roads Neural networks Vehicular ad hoc networks Deep reinforcement learning Automobiles

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CoLLM: A Collaborative LLM Inference Framework for Resource-Constrained Devices

CoLLM: A Collaborative LLM Inference Framework for Resource-...

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IEEE International Conference on Communications in China (ICCC)

作者： Jinrong Li Biao Han Sudan Li Xiaoyan Wang Jie Li College of Computer Science and Technology National University of Defense Technology Changsha China Graduate School of Science and Engineering Ibaraki University Hitachi Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Over the past few years, there has been notable advancement in Large Language Models (LLMs), leading to their extensive utilisation across various domains. Running large-scale LLMs usually necessitates processing capacity at the level of data-centers, which deters numerous potential applications from researchers. However, certain applications with great potential in LLM, such as the Internet of Things(IoT) data analysis and multi-robot collaboration, are typically constrained by lack of resources, specifically graphics processing units(GPUs). As a result, these devices fail to execute LLM inference. To tackle the aforementioned issues, we first investigate the problem of “Compute Bound” in devices with constrained resources, which are unavailable for hierarchical partitioning models. Furthermore, utilising the LLM tensor parallelization, we present a collaborative LLM inference framework on resource-constrained devices called CoLLM. In addition, we propose a minimal latency algorithm and an adaptive load balancing algorithm to optimize inference latency and balance energy consumption. (1) By considering the LLM model’s size, device resources, and network conditions, we can calculate the optimum number of collaborative devices to minimise inference latency. (2) CoLLM is capable of dynamically distributing computational workloads based on the target device’s status, balancing power consumption to extend overall working time. Experiments are conducted when the Llama2 model is executed on GPU-free devices such as Raspberry Pis. Evaluation results show that end-to-end inference speed outperforms current hierarchical LLM inference methods by a factor of $1.9 x-2.3 x$.

关键词： Adaptation models Tensors Power demand Computational modeling Heuristic algorithms Collaboration Load management

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Application of Artificial Intelligence in Malignant Tumors: A science Mapping Analysis

Application of Artificial Intelligence in Malignant Tumors: ...

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IEEE Control and System graduate Research Colloquium (ICSGRC)

作者： Samuel-Soma M. Ajibade Anwar P.P. Abdul Majeed Muhammed Basheer Jasser Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Ganesh Kumar Department of Computing and Information Systems School of Engineering & Technology Sunway University Petaling Jaja Selangor Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkiye Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia

ISBN: (数字)9798350386554

ISBN: (纸本)9798350386561

A science mapping analysis was performed on Scopus papers spanning from 2003 to 2022 to investigate the utilization of artificial intelligence techniques in malignant tumors research. The analysis unveiled a progressive rise in publications, amounting to a total of 206, with articles accounting for 55.34% and conference papers making up 44.66%. Zuherman Rustam and Universitas Indonesia made substantial contributions. India's exceptional performance might be due to the availability of financial support from prestigious organizations such as Universitas Indonesia, the National Natural science Foundation of China, and Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico. An investigation of social networks, specifically looking at collaborations between authors, revealed robust international cooperation, which improved the availability of resources and infrastructure. The hotspot research revealed the prominent terms to be genetic algorithms, diseases, and feature extraction. The cluster analysis identified three main areas of focus: Precision Health Analytics, Genomic Cancer Profiling, and Integrated AI Diagnosis. In summary, AIMTR research is actively tackling cancer and oncological concerns through the use of computational approaches, making substantial contributions to both societal and scientific progress.

关键词： Productivity Social networking (online) Genomics Organizations Market research Paper making Artificial intelligence Bioinformatics Cancer Genetic algorithms

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Advancing Haul Truck Safety: A Deep Neural Network-Based Driver Assistance System for Blind Spot Detection

Advancing Haul Truck Safety: A Deep Neural Network-Based Dri...

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Smart World Congress (SWC), IEEE

作者： Qaiser Anwar Muhammad Hanif Daisuke Shimotoku Hill H Kobayashi Kobayashi Lab Center for Spatial Information Science (CSIS) University of Tokyo Chiba Japan Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (GIKI) Topi Pakistan Kobayashi Lab Center for Spatial Information Science (CSIS) Information Technology Center (ITC) University of Tokyo Chiba Japan Kobayashi Lab Center for Spatial Information Science (CSIS) Information Technology Center (ITC) Graduate School of Frontier Sciences (GSFS) University of Tokyo Chiba Japan

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The utilization of haul trucks within the mining industry presents a myriad of safety challenges, primarily due to their considerable size. Haul trucks serve as indispensable assets in large-scale construction projects, owing to their unparalleled capacity for transporting heavy loads. Tailored specifically for such rigorous tasks, these trucks are restricted to traversing non-crowded roads to uphold safety standards. Nonetheless, their sheer size and design contribute to numerous blind spots, rendering them challenging to operate. In this study, we address the issue of blind spots in haul trucks by proposing a cutting-edge machine vision system. Our innovative approach integrates machine vision technology with advanced Deep Neural Network (DNN) algorithms to detect objects within the truck’s blind spots and promptly alert the driver in real time. Leveraging a comprehensive dataset of images, the DNN is trained to identify objects of varying sizes and shapes accurately. To facilitate seamless monitoring, our system incorporates a visual display unit installed in the truck’s cab, offering the driver a real-time panoramic view of the surroundings. Initial testing conducted in controlled laboratory settings yielded promising results, with the DNN algorithm demonstrating high accuracy in object detection. To validate our concept, we opted to employ a standard car as a surrogate for the haul truck. Equipped with four strategically positioned cameras providing a 360 -degree field of view, we scaled down the truck’s dimensions to match those of a car. Utilizing the D455 Intel RealSense camera, we precisely measure the distances of detected objects and issue alerts to the driver within a 4 -meter range via a built-in buzzer. Our system is further fortified with the inclusion of the Jetson Orin Nano developer kit. Real-world testing conducted in a moving vehicle showcased the effectiveness of our system, garnering favorable results. Our endeavor underscores a significant ste

关键词： YOLO Visualization Artificial neural networks Cameras Real-time systems Safety Automobiles Standards Vehicles Testing

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Proposing a visualized comparative review analysis model on tourism domain using Naïve Bayes classifier

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Procedia computer science 2023年 227卷 482-489页

作者： Albert Verasius Dian Sano Adriel Anderson Stefanus Evaristus Didik Madyatmadja Hendro Nindito Agung Purnomo Corinthias P.M. Sianipar Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta 11480 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia Department of Global Ecology Graduate School of Global Environmental Studies (GSGES) Division of Environmental Science and Technology Graduate School of Agriculture (GSA) Kyoto University Kyoto 606-8502 Japan

This study, using the Naïve Bayes classifier, proposes a new descriptive model for conducting a comparative review analysis on the tourism domain. The proposed model seeks to improve the understanding of tourists' experiences by visually representing their opinions between various tourist destinations. The model predicts the sentiment of tourist reviews using the Naive Bayes classifier, which is then used to generate visualizations that facilitate comparative analysis. The comparison takes two tourism sites as experimental examples. The results, in terms of accuracy, show 80% and 64% for the first and the second site respectively. The study discusses the model's implementation, effectiveness, and potential applications in the tourism industry. The findings of the study suggest that the proposed model can provide valuable insights into tourist experiences and help tourism stakeholders, such as the managements of tourism sites, the future or soon coming tourists to make informed decisions based on comparative analysis.

关键词： Comparative review analysis Naïve Bayes classifier tourism

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