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Using the Tsetlin Machine to Discover Fatality-based Transitions in Political Violence 3

Using the Tsetlin Machine to Discover Fatality-based Transit...

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3rd International Symposium on the Tsetlin Machine, ISTM 2024

作者： Hsu, Hsu-Chiang Bang-Jensen, Bree Colaresi, Michael P. Chrysanthis, Panos K. Zadorozhny, Vladimir I. Dept. of Informatics & Network Systems University of Pittsburgh United States Dept. of Political Science University of Pittsburgh United States Dept. of Computer Science University of Pittsburgh United States

ISBN: (纸本)9798331504984

There is considerable momentum across multiple continents to build and deploy systems that can usefully inform policy-makers and aid agencies about the timing, location, and scale of political violence. In this paper we introduce a framework that utilizes an architecture based on the Tsetlin Machine to detect notable transitions between lower and higher levels of political violence as measured by fatalities. We engineer input features based on the influential framework of horizontal inequalities (HI) and conflict history as they have been theorized to presage violence. However, we encounter the challenge that some classes are more or less distinguishable from others using TM-generated rules. We leverage the analytical structure of TMs and their weights to explain these clusters. To do so, we introduce the concept of a class spectrum and reduce the dimensions of these spectrums across classes and features with principal components analysis (PCA). This pipeline allows us to find transition (change) points between the fatality classes and cluster the classes into efficiently detectable groups. These tools provide both researchers and policy-makers a means of understanding similarities and differences in explanations across different conflict phases. ©2024 IEEE.

关键词： Data warehouses

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Topic Modeling Using Latent Dirichlet Allocation Method Based On Child Anecdotal Record Data 4

Topic Modeling Using Latent Dirichlet Allocation Method Base...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Fernandis, Rasio Swanjaya, Daniel Ramadhani, Risky Aswi Kasih, Patmi Sahertian, Julian Universitas Nusantara Pgri Kediri Faculty of Engineering and Computer Science Dept. Informatics Engineering Kediri Indonesia

ISBN: (纸本)9798350376111

Kindergarten education has a very important role in shaping the foundation of child development. For parents, see reports on the achievement of children's development at school through the results of each semester's assessment. The process of preparing teacher assessments uses 4 types of assessments, one of which is anecdotal record assessment. However, teachers experience problems in analyzing the topics in children's anecdotal notes so that the results become less clear. In addition, it is still done manually so it takes a long time to analyze. Assessment of children's anecdotal notes is an assessment in the form of a record of events that occur to children while at school. Anecdotal notes are in the form of text data, therefore this research aims to find out the topics in children's anecdotal notes data by utilizing topic modeling using the Latent Dirichlet Allocation (LDA) method. The children's anecdotal notes data is used as one dataset with a total of 276 collected directly from YBPK Sidorejo Kindergarten, after which data preprocessing is carried out in the form of case folding, remove punctuation, tokenizing, stopwords, and stemming. In the LDA topic modeling research using the Gensim library, 5 experiments were carried out by determining the number of different iterations, and displaying a range of 2-10 topics for each experiment. Analysis and evaluation using coherence score to determine the optimal number of topics by looking at the highest coherence score value in the experiment conducted. The analysis results in 3 topics with a coherence score of 0.4704 and 40 iterations or passes. © 2024 IEEE.

关键词： anecdotal record coherence score kindergarten Latent Dirichlet Allocation topic modeling

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Visual Coherence Loss for Coherent and Visually Grounded Story Generation 8

Visual Coherence Loss for Coherent and Visually Grounded Sto...

引用

8th Workshop on Representation Learning for NLP, RepL4NLP 2023, co-located with ACL 2023

作者： Hong, Xudong Demberg, Vera Sayeed, Asad Zheng, Qiankun Schiele, Bernt Dept. of Computer Vision and Machine Learning MPI Informatics Germany Dept. of Language Science and Technology Dept. of Computer Science Saarland University Germany Dept. of Philosophy Linguistics and Theory of Science University of Gothenburg Sweden Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9781959429777

Local coherence is essential for text generation models. We identify two important aspects of local coherence within the visual storytelling task: (1) the model needs to represent re-occurrences of characters within the image sequence in order to mention them correctly in the story;(2) character representations should enable us to find instances of the same characters and distinguish different characters. In this paper, we propose a loss function inspired by a linguistic theory of coherence for learning image sequence representations. We further propose combining features from an object detector and a face detector to construct stronger character features. To evaluate visual grounding that current reference-based metrics do not measure, we propose a character matching metric to check whether the models generate referring expressions correctly for characters in input image sequences. Experiments on a visual story generation dataset show that our proposed features and loss function are effective for generating more coherent and visually grounded stories. Our code is available at https://***/vwprompt/vcl. © 2023 Association for Computational Linguistics.

关键词： Electric grounding

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Collision-Risk-Aware Ship Routing 24

Collision-Risk-Aware Ship Routing

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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

作者： Michalski, Patrik Thomas Preuß, Niko Renz, Matthias Tritsarolis, Andreas Theodoridis, Yannis Pelekis, Nikos Dept. of Computer Science Kiel University Schleswig-Holstein Kiel Germany Dept. of Informatics University of Piraeus Piraeus Greece Dept. of Statistics & Insurance Science University of Piraeus Piraeus Greece

ISBN: (纸本)9798400711077

This paper addresses short-term Collision-Risk-Aware ship route planning while utilizing a deep learning-based Vessel Collision Risk Assessment and Forecasting (VCRA/F) framework to quantify risks. Lacking a clear boundary between risky and viable routes, we propose a Pareto-optimal search for alternative routes, balancing collision risk and voyage time. Our main contribution is a novel framework that integrates VCRA/F for Pareto-optimal route queries in dynamic environments. We model maritime routes using a hexagon-based graph network on the sea. Our experiments on real-world AIS data validate the effectiveness of Skyline-VCRA/F while highlighting areas for further improvement. © 2024 Copyright held by the owner/author(s).

关键词： Ships

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A Deep Ensemble Learning Approach for Venomous & Non-Venomous Serpents and Insect Species Identification 3

A Deep Ensemble Learning Approach for Venomous & Non-Venomou...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Samarasinghe, Buddhin Saroj Lakmali, K.B.N. Informatics Institute of Technology Dept. of Computer Science Colombo 06 Sri Lanka Informatics Institute of Technology Department of Computer Science Colombo 06 Sri Lanka

ISBN: (纸本)9798350302288

In recent years snake and insect attacks have become a huge problem worldwide. Most species have similar colors and shapes, which makes it hard to tell them apart using typical techniques. Similarly, identifying different species of bees and wasps can be challenging, especially for non-experts in the field. Therefore, developing a reliable and effective method for recognizing these animals is essential to reducing the issues caused by snake and insect attacks and supporting wildlife conservation efforts. The proposed method in this research utilizes deep ensemble learning and transfer learning techniques to identify snakes, wasps, and bees accurately. Deep ensemble learning involves combining several machine learning models to make better predictions, and transfer learning is a technique that allows pre-trained models to be re-used for new tasks, saving time and computational resources. The proposed method utilizes deep ensemble learning and uses three base transfer learning models, DenseNet201, MobileN etV2, and InceptionResN etV2, for classification. The study obtained a training accuracy of 93%. The research classified 11 types of snakes, four types of bees, and four types of wasps, including all 19 classes. Moreover, the author developed a mobile application for user interaction and utilized Flask API for the logic tier. The mobile application enables users to take pictures or upload images of a snake, wasp, or bee and identify the species accurately. This feature makes the proposed method more accessible to people who encounter these animals daily. Overall, this research represents a promising step towards developing an accurate and effective system for identifying snakes, wasps, and bees, with practical implications for human safety and biodiversity conservation. © 2023 IEEE.

关键词： Learning systems

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Indonesian Hate Speech and Abusive Tweets Classification with Deep Learning Pre-trained Language Models 6

Indonesian Hate Speech and Abusive Tweets Classification wit...

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6th International Conference on computer and informatics Engineering, IC2IE 2023

作者： Yulianto Darmawan, Bagus Tri Irnawan, Bassamtiano Renaufalgi Suzuki, Yoshimi Politeknik Negeri Jakarta Dept. of Computer and Informatics Engineering Depok Indonesia University of Yamanashi Dept. of Computer Science and Engineering Kofu Japan

ISBN: (纸本)9798350345162

Hate speech and abusive language on social media can spread massively and escalate into conflict between two or more individuals or parties. Previous studies about multi-label classification to identify whether the tweet contains hate speech, abusive language, or not have been conducted with machine learning techniques. They applied machine learning with a combination of feature extraction and achieved a good result in recognizing the tweet with hate speech content. In this research, we apply two approaches: first, we use vanilla pre-trained models of IndoBERT, IndoBERTweet, and Indonesian RoBERTa;second, we combine the three previous models with CNN. Our experiment shows that our proposed method has better results compared to previous research with the best performance achieved by IndoBERTweet+CNN with 93.9% Accuracy. © 2023 IEEE.

关键词： Abusive CNN Deep Learning Hate Speech IndoBERT IndoBERTweet Indonesian RoBERTa Indonesian Twitter

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI 13

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mahali, Muhammad Izzuddin Leu, Jenq-Shiou Achmad Sulistyo Putro, Nur Widyawan Prakosa, Setya Avian, Cries National Taiwan University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan Universitas Negeri Yogyakarta Dept. of Electronics and Informatics Engineering Education Yogyakarta Indonesia Universitas Gadjah Mada Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350373332

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems. © 2024 IEEE.

关键词： Fuzzy clustering

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The journey from 5G towards 6G 8

The journey from 5G towards 6G

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8th International Symposium on Electrical and Electronics Engineering, ISEEE 2023

作者： Dagiuklas, Tasos London South Bank University Dept. of Computer Science and Informatics London United Kingdom

ISBN: (纸本)9798350301670

This paper gives an overview of the journey from 5G towards 6G evolution. The 5G has been built across three main application verticals as defined by ITU, namely: Enhanced Mobile Broadband, Massive Machine Type Communications and Ultra-reliable Low Latency Communications (URRLC). To support these verticals, 5G has defined the following enablers: Massive MIMO, cloudification of network infrastructure, network automation, network slicing and edge cloud computing. It is expected that 5G will provide flexibility in terms of openness, mobility, programmability and agility and robustness in a standardized manner. The journey towards 6G will describe the limitations of 5G technologies and outlines the technology enablers for 6G. These enablers include smooth integration and interworking of Non-Terrestrial Networking technologies (NTN), use of Reconfigurable Intelligent Surfaces (RIS) and use of AI to orchestrate network and cloud resources. Additionally, the paper will give an overview of 6G research initiatives at both regional and international level. © 2023 IEEE.

关键词： 5G mobile communication systems

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SE-Half-UNet: Accurate and Low-Cost Retinal Vessel Segmentation from Fundus Images 6

SE-Half-UNet: Accurate and Low-Cost Retinal Vessel Segmentat...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Khaldi, Anouar Khaldi, Belal Bezziane, Mohamed Ben Hebbache, Khaled Guediri, Samir Hasan, Siham University of Kasdi Merbah Dept. Computer Science Ouargla Algeria Liap Laboratory University of El Oued El Oued39000 Algeria University of De Montfort Dept. Computer Science And Informatics Leicester United Kingdom

ISBN: (纸本)9798350350265

Accurate retinal vessel segmentation from eye fundus images is a major challenge in automated methods. U-Net has been widely used in medical image segmentation, and its effectiveness has led researchers to propose various U-Net variants. However, most of these variants are resource-intensive, suggesting that further development is needed. This study introduces a novel approach called SE-Half-UNet, which derives from the Half-UNet framework by incorporating the Squeeze-and-Excitation (SE) Module to bolster the network's representative capacity by emphasizing channel relationships within feature maps. Our method achieves superior performance in retinal vessel segmentation, with a high F1-Score of 0.8303 and 0.8372 for DRIVE and CHASE_DB datasets, respectively. This new method facilitates the accurate analysis of eye images and encourages computer research advances. © 2024 IEEE.

关键词： Semantic Segmentation

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Advancing Healthcare Diagnostics with a Hybrid AI Model 4

Advancing Healthcare Diagnostics with a Hybrid AI Model

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Jihane, Benbrik Salma, Rattal Kamal, Ghoumid Miloud, Ar-Reyouchi El Ensao Mohammed i University Dept. Electronics Informatics and Telecommunications Oujda Morocco Abdelmalek Essaâdi University Dept. Telecommunication and Computer Science Tétouan Morocco

ISBN: (纸本)9798331523923

Artificial intelligence (AI) has significantly impacted the field of healthcare by advancing medical diagnostics and decision-making processes. This paper presents a unified framework that integrates Convolutional Multi-Layer Perceptrons (Conv MLP), Convolutional Neural Networks (ConvNet), and Vision Transformers (ViTs) to address two critical tasks: sentiment analysis of medical reports and medical image recognition. By leveraging the distinct strengths of each architecture, the proposed hybrid model improves performance, effectively balancing computational efficiency, localized feature extraction, and global dependency modeling. The findings demonstrate that the model reduces task-specific errors and enhances convergence rates, achieving superior results in sentiment and image recognition tasks. This study advances the field of medical AI by overcoming the limitations of single-architecture models and establishing the feasibility of hybrid systems for robust, multi-modal applications. © 2025 IEEE.

关键词： Multilayer neural networks

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