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Journal of Applied Logics 2024年第1期11卷 85-123页

作者： Kern-Isberner, Gabriele Spohn, Wolfgang Dept. of Computer Science TU Dortmund Dortmund44221 Germany Dept. of Philosophy University of Konstanz Konstanz78457 Germany

This paper presents a broad view on inductive reasoning by embedding it in theories of epistemic states, conditionals, and belief revision. More precisely, we consider inductive reasoning as a specific case of belief revision on epistemic states which include conditionals as a basic means for representing beliefs. We present a general framework for inductive reasoning from conditional belief bases that also allows for taking background beliefs into account, and illustrate this by probabilistic reasoning based on optimum entropy as well as by rankingtheoretic reasoning based on so-called c-revision. We explain the philosophical perspective behind our approach, and we illustrate its constructive usefulness as well as its integrating power. © 2024, College Publications. All rights reserved.

关键词： Philosophical aspects

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Real-time Semantic Healthcare System: Visual Risks Identification for Elders and Children

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Informatica (Slovenia) 2024年第14期48卷 65-82页

作者： Belkebir, Malak Maarouk, Toufik Messaoud Nini, Brahim 2 University of Oum el Bouaghi Oum el Bouaghi Algeria ICOSI Laboratory Dept. of Mathematics and Computer Science Khenchela University Khenchela Algeria

Deep learning and data-driven approaches are commonly used to avoid accidents involving elders and children. However, existing models are limited by a semantic gap, hindering their ability to infer new risks that have not been previously trained. In this paper, a real-time healthcare system is developed to identify and infer visual risks in surveillance videos for elders and children. The system consists of three main modules:"visual information extraction," which leverages advancements in artificial vision techniques such as GCD and IoU for relationship detection, YOLO for object detection, and ResNet18 for scene recognition;"ontology modeling," where a new high-level ontology named"Risks-Identification-Onto" is constructed based on FOL and DLs;and"risk identification," where the system infers risks by deducing new knowledge through the reasoning of generated formal rules over the data-driven techniques. Additionally, the system generates a high-level semantic description of the risky situation. Four common risk scenarios -"Hurt,""Burn,""Existing-in-dangerous-places," and"Hit"- are selected to evaluate the effectiveness of the proposed system. Evaluation is conducted using the Charades and A2D datasets, each including 9,848 and 3,782 indoor activity videos. It demonstrate the system’s efficiency in identifying and inferring risks in real-time with an accuracy ranging from 97.61% to 99.43% for each scenario. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Ontology

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Advancements in Sentiment Analysis for the Algerian Dialect: A Comprehensive Review 6

Advancements in Sentiment Analysis for the Algerian Dialect:...

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

作者： Remadnia, Ouahiba Maazouzi, Faiz University of Souk Ahras Laboratory of Computer Science And Mathematics Souk Ahras Algeria University of Souk Ahras Dept. of Computer Science Souk Ahras Algeria

ISBN: (纸本)9798350350265

Sentiment analysis (SA) delves into the examination of opinions, feelings, and emotions expressed through written language, making it a very dynamic area of research in natural language processing (NLP). The Algerian dialect, known for its complexity and diversity, presents huge challenges to SA. Researchers are working to improve methodologies to achieve enhanced accuracy. This paper critically reviewed the progress of the Algerian dialect (AlgD) from 2017 to 2024, aiming to provide a full analysis of 28 research articles, focusing on the significant contributions to the field, key challenges, and the effectiveness of current models and methods. Our comprehensive review highlights the essential role of advanced analytical techniques in enriching our understanding and interpretation of AlgD, promising significant advances in linguistic and computational research. © 2024 IEEE.

关键词： Sentiment analysis

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Enhancing Argument Summarization: Prioritizing Exhaustiveness in Key Point Generation and Introducing an Automatic Coverage Evaluation Metric

Enhancing Argument Summarization: Prioritizing Exhaustivenes...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Khosravani, Mohammad Huang, Chenyang Trabelsi, Amine Dept. of Computer Science Lakehead University Canada Amii Dept. of Computing Science University of Alberta Canada Dept. of Computer Science Université de Sherbrooke Canada

ISBN: (纸本)9798891761148

The proliferation of social media platforms has given rise to the amount of online debates and arguments. Consequently, the need for automatic summarization methods for such debates is imperative, however this area of summarization is rather understudied. The Key Point Analysis (KPA) task formulates argument summarization as representing the summary of a large collection of arguments in the form of concise sentences in bullet-style format, called key points. A sub-task of KPA, called Key Point Generation (KPG), focuses on generating these key points given the arguments. This paper introduces a novel extractive approach for key point generation, that outperforms previous state-of-the-art methods for the task. Our method utilizes an extractive clustering based approach that offers concise, high quality generated key points with higher coverage of reference summaries, and less redundant outputs. In addition, we show that the existing evaluation metrics for summarization such as ROUGE are incapable of differentiating between generated key points of different qualities. To this end, we propose a new evaluation metric for assessing the generated key points by their coverage. Our code can be accessed online. © 2024 Association for Computational Linguistics.

关键词： Quality control

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CONVOLUTIONAL NEURAL NETWORK ALGORITHM FOR CANCELLABLE FACE TEMPLATE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2024年第1期86卷 155-166页

作者： Alwan, Hiba Basim Ku-Mahamud, Ku Ruhana Dept. of Computer Science University of Technology Iraq School of Computing Universiti Utara Malaysia Malaysia

Biometrics systems utilizing hand geometry, fingerprint, iris, face, palm print, voice, gesture, and palm print have been utilised for authentication purposes. Through these templates, the face template is suggested as the strongest. However, problems still exist in face-matching implementation which affects biometric security. Thus, in this paper, a new cancellable face image algorithm using the Convolution Neural Network (CNN) feature extraction technique and Winner-Takes-All hashing method is proposed. The algorithm is to overcome the problem of matching which will enhance its security. ORL and Yale datasets were utilized in the evaluation of the proposed algorithm. Several common algorithms were utilised in the comparison based on Equal Error Ratio (ERR) measurement. The outcomes displayed the verification performance of the unimportant differences with EER = 0.95% and 3.25% for the extended version of the face dataset for the Yale and ORL face datasets, respectively. The acceptable implementation of the proposed algorithm demonstrates that it can be used for the security purpose of biometric trait tasks. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Convolution

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An Effective Deep Learning Approach for Kidney Stone Detection in CT Scan Images using Convolutional Neural Network

An Effective Deep Learning Approach for Kidney Stone Detecti...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Islam, Md. Nurul Khan Chowdhury, Md. Fuad Hasan Nusrat, Fariha University of Scholars Dept. of Computer Science and Engineering Dhaka Bangladesh United International University Dept. of Computer Science and Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

Kidney stones represent a significant global health concern, frequently causing individuals to seek immediate medical care due to intense pain. Radiological imaging modalities are one of the most common modes of diagnosis, but specialist interpretation is always important for accuracy. In this paper, we present computer vision in an automated kidney stone detection system that involves five stages: loading the dataset, preprocessing data, loading the InceptionV3 model, predicting, and evaluating performance. Various methods of preprocessing and data augmentation were utilized to make the model less biased and more robust. This study uses the InceptionV3 architecture to classify kidney stones in CT scan images of KUB (Kidney, Ureter, and Bladder). We utilize a total dataset of 433 participants, either diagnosed with kidney stones or in a normal state. 278 cases are diagnosed with kidney stones, and 165 are normal cases. The proposed approach achieved an impressive 98.55% accuracy across performance metrics when evaluated on an independent test dataset. © 2024 IEEE.

关键词： computerized tomography

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A comprehensive step of XAI for Bengali and English text detection and recognition from natural scene images 2

A comprehensive step of XAI for Bengali and English text det...

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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

作者： Dutta, Mithun Das, Utpol Kanti Das, Jugal Krishna Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350367461

A critical first step in many applications, including augmented reality, document analysis, and scene comprehension, is text detection from images. Even though text identification for the English language has advanced significantly, there are particular difficulties when applying these methods to languages such as Bengali because of variations in script, morphology, and typographic characteristics. This article offers a wonderful method for leveraging the Faster R-CNN framework to identify and categorize text from images that contain Bengali and English sentences. The study utilizes a dataset named "Bengali and English text image dataset (BETID)"that consists of images with text in both Bengali and English scripts, taken from a variety of sources such as books, documents, natural scenes, internet images, and landscapes. Pre-processing methods are used to improve the resilience and generalization of the model, including image scaling, normalization, and augmentation. The Faster R-CNN architecture is tuned and trained to identify text regions in images and categorize Bengali or English text. The performance of the text Classification model is evaluated using evaluation measures such as accuracy on the BETID dataset. The proposed Faster R-CNN model achieved an average of 97.90% accuracy, which is better than other deep learning models. Empirical findings showcase the capacity of transfer learning to alleviate data scarcity concerns and enhance text detection precision for low-resource languages such as Bengali.) © 2024 IEEE.

关键词： Deep learning

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Characterizing Evolutionary Trends in Temporal Knowledge Graphs with Linear Temporal Logic

Characterizing Evolutionary Trends in Temporal Knowledge Gra...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Fionda, Valeria Pirro, Giuseppe University of Calabria Dept. of Mathematics and Computer Science Rende Italy

ISBN: (纸本)9798350324457

Temporal changes in data set the need for the development of knowledge representation techniques able to capture these dynamics. Temporal Knowledge Graphs (TKGs) have emerged as a reference tool to represent dynamic, structured and machine-interpretable knowledge that evolves over time. However, characterizing the evolution of TKGs in a meaningful and human-understandable way remains a significant challenge. In this short paper, we argue that Linear Temporal Logic (LTL), with its expressive temporal constructs, is a promising approach for capturing a wide range of temporal behaviors and high-level evolutionary patterns occurring in TKGs. © 2023 IEEE.

关键词： Evolution Patterns Linear Temporal Logic Temporal Knowledge Graphs

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Fine-Tuning BERT for Aspect Extraction in Multi-domain ABSA

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Informatica (Slovenia) 2023年第9期47卷 123-131页

作者： Akram, Arwa Sabir, Aliea Faculty of Computer Science and Information Technology Computer Information System Dept. University of Basrah Basrah Iraq

Aspect extraction plays a crucial role in understanding the fine-grained nuances of text data, allowing businesses and researchers to gain deeper insights into customer opinions, sentiment distributions, and preferences. This study presents a BERT-based framework for aspect extraction in ABSA and evaluates its performance. Our research focuses on the comprehensive analysis of aspect extraction as we test our method using the SEMEVAL dataset of various consumer evaluations across diverse domains, including laptops, restaurants, and Twitter. By fine-tuning BERT on a large dataset, we aim to overcome the limitations of traditional approaches and improve the accuracy and efficiency of aspect extraction in ABSA. The experimental findings provide evidence of the efficacy of our methodology with a noteworthy aspect extraction accuracy of 0.98, highlighting its capacity to properly and consistently extract features. The article also explores the applicability of our approach to new domains and its possible applications in real-world scenarios. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Sentiment analysis

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Soulbound Token for Covid-19 Vaccination Certification

Soulbound Token for Covid-19 Vaccination Certification

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2023 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Lunesu, Maria Ilaria Tonelli, Roberto Pinna, Andrea Sansoni, Simone University of Cagliari Dept. of Mathematics and Computer Science Cagliari Italy

ISBN: (纸本)9781665453813

Soulbound Tokens were introduced last May by Vitalik Buterin as a new kind of non-fungible, non-transferable token in the Ethereum ecosystem. The main idea behind such tokens is that they are born and die at the same address without ever changing ownership. Because there is typically no one-to-one correspondence between blockchain addresses and people, asserting that such new tokens are associated with a person for life is a strong assertion. Nevertheless, these new tokens promise to envision practical applications related to personal reputation or ownership of specific, non-transferable assets, and their real-world potential applications remain largely unexplored. In this paper, we present a practical application of such tokens, investigating a use case where a real-world problem largely impacting modern life has been exploited: COVID vaccine certification. The approach can easily be extended to other vaccination cases and relies on the use of a DApp where Soulbound Tokens have been implemented. In our scheme, two main actors are involved. The system administrator, represented by a local medical organization, is able to insert into a database the list of vaccines for which certification will be sent, and the citizen, after receiving the vaccination, will be able to receive and visualize the certifications provided by the first actor. © 2023 IEEE.

关键词： Vaccines

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