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Introduction to this special section: Geophysical methods in archaeology

Leading Edge 2024年第10期43卷 655-656页

作者： Krahenbuhl, Richard A. Wilt, Michael Colorado School of Mines Geophysics Department Humanitarian Engineering and Science Interdisciplinary Graduate Program GoldenCO United States Lawrence Berkeley National Laboratory Earth Science Division BerkeleyCA United States

Archaeogeophysics, i.e., the application and integration of geophysics into archaeological investigations, is an exciting and growing field of study and an international collaboration at the intersection of the physical and social sciences. Through the incorporation of ground-and drone-based geophysical and remote sensing tools, these collaborative teams continue to advance the field of archaeology by rapidly and accurately revealing hidden features of the past and helping to focus active archaeological digs. Equally relevant, yet often unnoticed, the inclusion of noninvasive geophysical tools likewise provides a means to understand and preserve historically and culturally sensitive sites without the need for extensive and potentially destructive excavations. © 2024 by The Society of Exploration Geophysicists.

关键词： Geophysics

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A Blockchain-assisted Authentication Protocol for RFID-enabled Supply Chain Management System

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IEEE Transactions on Network science and engineering 2025年第4期12卷 3108-3117页

作者： Tariq, Tayyaba Obaidat, Mohammad S. Kuo, Wen-Chung Mahmood, Khalid Ayub, Muhammad Faizan Alenazi, Mohammed J.F National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Riyadh11451 Saudi Arabia

In the dynamic domain of supply chain management, integrating Radio-Frequency Identification (RFID) technology with blockchain technology represents a significant leap forward. This integration has created a blockchain-assisted RFID-enabled Supply Chain Management System (SCMS). With its ability to utilize electromagnetic fields to identify and track tags attached to objects, RFID technology has revolutionized product management and tracking within supply chains. (SCMS) transmits tracking and product management information through public communication channels. However, (SCMS)'s communication on these channels is vulnerable to various security attacks and privacy challenges. Numerous authentication protocols have recently been proposed to tackle these security and privacy challenges. Unfortunately, most protocols are susceptible to different security attacks, such as tag or reader impersonation, denial of service, physical cloning, desynchronization attacks, etc. Therefore, we have proposed an authentication protocol for an RFID-enabled (SCMS) that also leverages blockchain technology. The integration of blockchain technology ensures data integrity, immutability and transparency across each department involved in (SCMS). In the proposed protocol, we also employ a Physically Unclonable Function (PUF) to secure (SCMS) against physical cloning attacks. We validate the security of the proposed protocol through both informal and formal analysis. The informal analysis confirms that our protocol significantly enhances security and efficiency. Moreover, a performance analysis of the proposed protocol against various competing existing protocols shows its superior performance. Notably, the proposed protocol substantially reduces computational and communication costs by 24.38% and 8.03%, respectively, which underscores its enhanced performance and resource efficiency. © 2013 IEEE.

关键词： Efficiency

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Audio-visual keyword transformer for unconstrained sentence-level keyword spotting

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 142-152页

作者： Yidi Li Jiale Ren Yawei Wang Guoquan Wang Xia Li Hong Liu Key Laboratory of Machine Perception Peking UniversityShenzhen Graduate SchoolShenzhenChina College of Electronics and Information Engineering Sichuan UniversityChengduChina Department of Computer Science ETH ZurichZurichSwitzerland

As one of the most effective methods to improve the accuracy and robustness of speech tasks,the audio-visual fusion approach has recently been introduced into the field of Keyword Spotting(KWS).However,existing audio-visual keyword spotting models are limited to detecting isolated words,while keyword spotting for unconstrained speech is still a challenging *** this end,an Audio-Visual Keyword Transformer(AVKT)network is proposed to spot keywords in unconstrained video *** authors present a transformer classifier with learnable CLS tokens to extract distinctive keyword features from the variable-length audio and visual *** outputs of audio and visual branches are combined in a decision fusion *** humans can easily notice whether a keyword appears in a sentence or not,our AVKT network can detect whether a video clip with a spoken sentence contains a pre-specified ***,the position of the keyword is localised in the attention map without additional position ***-imental results on the LRS2-KWS dataset and our newly collected PKU-KWS dataset show that the accuracy of AVKT exceeded 99%in clean scenes and 85%in extremely noisy *** code is available at https://***/jialeren/AVKT.

关键词： artificial intelligence multimodal approaches natural language processing neural network speech processing

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GeoRefineNet: A Multistage Framework for Enhanced Cephalometric Landmark Detection in CBCT Images Using 3D Geometric Information 17th

GeoRefineNet: A Multistage Framework for Enhanced Cephalome...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Viriyasaranon, Thanaporn Ma, Serie Choi, Jang-Hwan Department of Computational Medicine Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Artificial Intelligence Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9789819609000

The precise detection of cephalometric landmarks on two-dimensional (2D) radiographs or three-dimensional (3D) computed tomography (CT) images is a fundamental step in various medical fields, especially in research on orthodontics and maxillofacial surgery. Deep learning-based detectors have demonstrated remarkable accuracy in 2D cephalometric analysis, whereas conventional single-view approaches are limited by their reliance on information from a single perspective. This study proposes GeoRefineNet, a novel multistage framework that leverages information from multiple CT scans acquired at various angles. By incorporating geometric knowledge through a 3D heatmap reconstruction process, GeoRefineNet improves robustness, accuracy, and adaptability to various cephalometric configurations. The proposed framework predicts 3D landmark positions on CT images, effectively addressing challenges associated with high-dimensional input data and limited training examples. GeoRefineNet surpasses the existing state-of-the-art models in the 2D and 3D domains, as demonstrated by its superior performance on numerical and clinical datasets. These findings indicate that GeoRefineNet offers a promising avenue for improving the accuracy and reliability of cephalometric landmark detection fostering further advances in clinical diagnosis and treatment planning. Our code is available at https://***/Thanaporn09/***. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： computerized tomography

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Classification Prediction of Heart Disease Using Machine Learning Techniques 4

Classification Prediction of Heart Disease Using Machine Lea...

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Setiawan, Kevis Jonathan Beratha, Putu Satria Anggereainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

Heart disease is also called a common one of global health concerns. A lot of research has been done before to predict someone whether has a heart disease or not by machine learning. In this study, we use five machine learning techniques as comparison which machine learning technique has a most accuracy to recognize heart disease in someone's condition. In this case, we are using UCI Cleveland Dataset as a sample and the result shows that the Support Vector Machine and K-Nearest Neighbor gives the most accuracy which is 85% along with many aspects respectively. © 2023 IEEE.

关键词： Support vector machines

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Investigation of Colors Categorized into Color Names on a Liquid Crystal Display to Enhance CAPTCHAs

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International Journal of the Society of Material engineering for Resources 2022年第2期25卷 212-217页

作者： Ishizawa, Chikako Kobayashi, Nozomu Kageyama, Yoichi Department of Mathematical Science and Electrical-Electronic-Computer Engineering Graduate School of Engineering Science Akita University Akita010-8502 Japan

This study focuses on the human ability to tolerate subtle diff erences in color and categorize similar colors into a single mass to enhance completely automated public turing test to tell computers and humans apart (CAPTCHA). First, an experiment was conducted to check whether the color selection condition could control the color categorization results. Consequently, it was clarifi ed that when the selection condition was added, the color to be categorized changed, implying the possibility of controlling the categorized result. Subsequently, an investigation was conducted to clarify the range of liquid crystal display colors categorized as major color names. As a result, it was clarified that the hue angle size of the colors categorized by more than half of the participants was sometimes greater than the hue angle size between the primary and complementary colors. It was also clarifi ed that among the categorized colors, there were categorized colors in the two color names. Finally, results from the fi rst and second experiments were collated. It was confi rmed that by using the categorized color range and selection conditions, it is possible to obtain an answer with a color name that is diff erent from the original color name. These results suggest that the ability of humans to categorize similar colors into a single mass enhances CAPTCHA. © 2022 Society of Materials engineering for Resources of Japan. All rights reserved.

关键词： Color

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Ship Navigational Status Classification Based on the Geometrical and Spatiotemporal Features of the AIS-Generated Trajectory 9

Ship Navigational Status Classification Based on the Geometr...

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9th International Conference on Big Data Analytics, ICBDA 2024

作者： Wijaya, Wayan Mahardhika Nakamura, Yasuhiro Graduate School of Science and Engineering National Defense Academy Yokosuka Japan National Defense Academy Department of Computer Science Yokosuka Japan

ISBN: (纸本)9798350352962

Studies on the Automatic Identification System (AIS) data mining have proven the feasibility of discovering knowledge from the AIS tracks to understand vessels' behavior. One of its purposes is to help maritime surveillance efforts for safety and security matters. Research on vessel behavior often relies on the spatiotemporal features of the AIS tracks, while human operators recognize vessel movement by comprehending their trajectory patterns. Additionally, many works do not comprehensively discuss a thorough AIS data preprocessing stage before implementing their novelty method to analyze raw AIS data. Thus, this paper proposes a trajectory segmentation technique, a data preprocessing procedure, for AIS data to provide an analytics-ready dataset that facilitates a practical implementation of AIS-based vessel behavior analytics. The approach utilizes the AIS-generated vessel trajectories' spatiotemporal and geometrical features to split a ship trajectory into segments according to its navigational status, such as stopping and underway. The spatiotemporal features are speed, course, heading, and position timestamp, while the geometrical features include the diameter and bounding box of the trajectory. Every trajectory is divided into stopping and underway segments. Stopping segment extraction aims to facilitate stopping event detection and classification, which helps understand vessel activities in port areas. Mean-while, the underway segment is dedicated to streamlining vessel movement analytics for loitering detection and other anomalies in maritime traffic. The experiment result demonstrates the efficacy of the method. It outperformed the existing stopping segments classification approach with 0.98 accuracy and improved the currently published loitering detection to 0.97 accuracy and 0.91 F-score. © 2024 IEEE.

关键词： Ships

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AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses

AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of O...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Lu, Xiaotian Li, Jiyi Takeuchi, Koh Kashima, Hisashi Department of Intelligence Science and Technology Graduate School of Informatics Kyoto University Kyoto Japan Department of Computer Science and Engineering University of Yamanashi Kofu Japan

ISBN: (纸本)9798891761681

Question answering (QA) tasks have been extensively studied in the field of natural language processing (NLP). Answers to open-ended questions are highly diverse and difficult to quantify, and cannot be simply evaluated as correct or incorrect, unlike close-ended questions with definitive answers. While large language models (LLMs) have demonstrated strong capabilities across various tasks, they exhibit relatively weaker performance in evaluating answers to open-ended questions. In this study, we propose a method that leverages LLMs and the analytic hierarchy process (AHP) to assess answers to open-ended questions. We utilized LLMs to generate multiple evaluation criteria for a question. Subsequently, answers were subjected to pairwise comparisons under each criterion with LLMs, and scores for each answer were calculated in the AHP. We conducted experiments on four datasets using both ChatGPT-3.5-turbo and GPT-4. Our results indicate that our approach more closely aligns with human judgment compared to the four baselines. Additionally, we explored the impact of the number of criteria, variations in models, and differences in datasets on the results. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Bending the Rule: Naphthobisthiadiazole-Based Terpolymers with Both High Solubility and Crystallinity for Organic Photovoltaics

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ACS Applied Polymer Materials 2024年第5期6卷 2586-2593页

作者： Chou, Li-Hui Chen, Fengkun Nakano, Kyohei Yamanaka, Kodai Mikie, Tsubasa Saito, Masahiko Liu, Cheng-Liang Tajima, Keisuke Osaka, Itaru Department of Applied Chemistry Graduate School of Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8527 Japan Department of Chemical and Materials Engineering National Central University Taoyuan32001 Taiwan Saitama Wako351-0198 Japan Applied Chemistry Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8527 Japan Department of Materials Science and Engineering National Taiwan University Taipei10617 Taiwan

Achieving a good balance between the crystallinity and solubility is of great importance in the design of π-conjugated polymers. Here, we synthesized a series of terpolymers in which small fractions of a planar V-shaped fused ring, triphenyleno[1,2-c:7,8-c′]bis([1,2,5]thiadiazole) (TPTz), are incorporated into the linear-shaped backbone of a highly crystalline polymer based on naphtho[1,2-c:5,6-c′]bis([1,2,5]thiadiazole) (NTz), PNTz4T. Due to the "bending" structure induced by the TPTz unit, the terpolymers had greatly improved solubility by about 5-9 times compared to the linear-shaped PNTz4T. In particular, the terpolymers preserved the crystallinity of PNTz4T to a considerable extent with a higher fraction of face-on oriented domains in films. As a result, the highly solution-processable terpolymers exhibited high photovoltaic performances and slightly outperformed PNTz4T. Our results indicate that the incorporation of a V-shaped unit into a linearly shaped polymer is a good strategy to develop π-conjugated polymers with high solubility and crystallinity. © 2024 American Chemical Society.

关键词： Solubility

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Hybrid approach sentiment analysis using Transformer-LSTM in the Indonesian language 7

Hybrid approach sentiment analysis using Transformer-LSTM in...

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7th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2024

作者： Karentia, Angel Winaya, Ferdinand Suhartono, Derwin BINUS Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331519643

Sentiment analysis is widely used as a tool to find valuable insight from texts without explicitly expressed. Lots of techniques have already been used to get it but there still have shortcomings in data source or the model strategy itself. Indonesian language approximately has 199 million speakers across the world where 44 million speakers natively. Even with that great number, the resources for the Indonesian language's natural language processing are still limited, and hard to find the perfect way to define sentiment analysis in the Indonesian language. The state-of-the-art sentiment analysis method uses LSTM on a small corpus while the best in town is Transformer where it's easy to transfer learning from the Transformer pre-trained model into specific tasks. From this potential, combining the Transformer pre-trained model with LSTM can be an innovative strategy. This research compared the hybrid model of Transformer-LSTM build using three Indonesian languages' Transformer-based pre-trained model on sentiment analysis task which can surpass the Transformer model with the highest increased 2.40% accuracy and 3.02% on F1 score. © 2024 IEEE.

关键词： Emotion Recognition

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