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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Ahat, Arzigul Firkat, Eksan Yuan, Xiaming Zhu, Jihong Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi China Department of Precision Instrument Tsinghua University Beijing China

In off-road scenes, the fusion of dual-LiDAR data is crucial for ensuring the accuracy of environmental perception in autonomous vehicles. The terrain in off-road scenes is complex and filled with unstructured information. This leads to significant noise and a lack of distinct structural features in the point cloud data, making traditional point cloud registration methods difficult to apply. To address these issues posed by complex off-road scenes, we propose a novel point cloud fusion framework named Off-Fusion. We first filter and segment the ground in the input point cloud data, focusing on preserving the core features of the ground while effectively removing noise caused by the terrain. Next, we propose a robust and efficient feature point extraction method based on voxel division and curvature weighting, ensuring extracting meaningful and representative feature points from complex off-road scenes. Based on this, we use feature matching to calculate rough relative transformation pairs, providing a high-quality starting position for the Iterative Closest Point (ICP) algorithm, effectively avoiding local optima. Finally, by combining the kd-tree accelerated ICP algorithm, we achieve precise point cloud registration, successfully calculating the optimal rotation and translation matrix between the two LiDARs. The experimental results show that our method significantly improves the quality and speed of data fusion. Compared to some of the most advanced methods, it performs better in off-road scenes, achieving the best results. IEEE

关键词： data mining

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Label Confidence Weighted Learning for Target-level Sentence Simplification

Label Confidence Weighted Learning for Target-level Sentence...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Qiu, Xinying Zhang, Jingshen Department of Computer Science School of Information Science and Technology Guangdong University of Foreign Studies China

ISBN: (纸本)9798891761643

Multi-level sentence simplification generates simplified sentences with varying language proficiency levels. We propose Label Confidence Weighted Learning (LCWL), a novel approach that incorporates a label confidence weighting scheme in the training loss of the encoder-decoder model, setting it apart from existing confidence-weighting methods primarily designed for classification. Experimentation on English grade-level simplification dataset shows that LCWL outperforms state-of-the-art unsupervised baselines. Fine-tuning the LCWL model on in-domain data and combining with Symmetric Cross Entropy (SCE) consistently delivers better simplifications compared to strong supervised methods. Our results highlight the effectiveness of label confidence weighting techniques for text simplification tasks with encoder-decoder architectures. © 2024 Association for Computational Linguistics.

关键词： Decoding

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Rust's Memory Safety Model: An Evaluation of Its Effectiveness in Preventing Common Vulnerabilities 8

Rust's Memory Safety Model: An Evaluation of Its Effectivene...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Oey, Kevin Andrian Santoso Kwee, Catherine Chua, William Nabiilah, Ghinaa Zain Rojali Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Ensuring safe program operation is crucial for systems programming, and memory safety is a significant aspect of this. This paper assesses the memory safety strategies used in Rust and C++ programming languages. Rust is a modern language that has become popular due to its focus on safety, performance, and concurrency, whereas C++ is known for its efficiency and low-level programming capabilities. The analysis compares and contrasts the memory safety features of both languages, including Rust's ownership and borrowing model and C++'s manual memory management techniques. Additionally, the study examines the development tools available for both languages and compares ease of use and productivity. The analysis shows that Rust has several memory safety advantages over C++. Rust's ownership and borrowing model enables safe memory management without requiring manual intervention. By enforcing strict rules for memory usage, the compiler in Rust prevents typical memory-related errors like null and dangling pointers. However, in contrast to Rust, C++ provides more low-level hardware control but needs manual memory management, exposing it to memory errors such as buffer overflows and memory leaks. Moreover, Rust's memory safety features simplify the development of secure and dependable code, especially for applications with crucial security requirements. In contrast, C++ demands significant memory management expertise to avoid memory-related errors. In conclusion, Rust offers several memory safety benefits over C++, making it a superior choice for applications with critical safety needs. However, the selection of language ultimately depends on the specific project requirements and the development team's experience and preferences. © 2023 The Authors. Published by Elsevier B.V.

关键词： C++ programming Language Memory Safety Rust programming Language Security Software Systems programming

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Comparing Age Estimation with CNN and EfficientNetV2B1 8

Comparing Age Estimation with CNN and EfficientNetV2B1

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Christopher, Marco Vito Wahid, Abdul Nabiilah, Ghinaa Zain Rojali Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Age estimation from facial images is a challenging topic in computer vision since it can automatically label the human face with an exact age according to various physical or biological characteristics, such as facial structure, spots, and wrinkles. Additionally, it has substantial applications in many fields, including healthcare, security, entertainment, and education. There are a lot of techniques to estimate age, but the most popular one is the convolutional neural network (CNN), which offers high accuracy but needs a lot of training time and much more labeled data to achieve it. Another popular technique is transfer learning with feature extraction, which can provide higher accuracy with faster training time because it uses a pre-trained model rather than building one from scratch but has limited flexibility and a substantial risk of overfitting. Due to the advantages and disadvantages of both methods, this study analyzes and compares the effectiveness of transfer learning and CNN methods for age estimation from facial images. Using R-squared and RMSE as test metrics, the results show that the CNN method offers a better process and accuracy than the transfer learning method, with a higher R-squared value and a lower RMSE value. © 2023 The Authors. Published by Elsevier B.V.

关键词： age estimation convolutional neural network facial images transfer learning

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Domain Adaptation in Color-Shifted datasets Through Adversarial Learning Balancing 4

Domain Adaptation in Color-Shifted Datasets Through Adversar...

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

作者： Hidayaturrahman Trisetyarso, Agung Kartowisastro, Iman Herwidiana Budiharto, Widodo School of Computer Science Bina Nusatara University Mathematics Department Jakarta11480 Indonesia Bina Nusatara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Computer Engineering Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Due to the disparity in the levels of difficulty presented by the several tasks, doing domain adaptation in an adversarial way may result in an imbalanced learning process. In the MNIST dataset, this phenomenon also manifests itself in the form of domain adaptation for color-shifted distribution. In this particular situation, the domain classifier has a higher tendency to fit more quickly, but the category classifier fits quite poorly in the learning process. In order to address this problem, a new hyper-parameter has been added to the loss function in order to strike a compromise between the learning speed of the domain and the categorical classifier. By using this technique, the categorical classifier may better match the data while still maintaining the same level of performance as the domain classifier. In order to determine whether or not making use of this hyper-parameter is useful, the phenomena in question is examined using three distinct color-shifted settings. Following the evaluations, it was discovered that the newly introduced hyper-parameter is capable of coping with imbalanced learning while simultaneously engaging in domain adaptation. © 2023 IEEE.

关键词： Color

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Diverse Geographical Regions Based Biodiversity Conservation by LiDAR Image with Deep Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 738-749页

作者： Babu C. N, Gireesh Reddy, Chilukala Mahender Kumar, Mungara Kiran Nagaraju, Komatigunta Maheswari, G. Uma Kiran, Ajmeera Department of Computer Science and Engineering BMS Institute of Technology & amp Management Karnataka Bengaluru India Department of Artificial Intelligence and Data Science GITAM (Deemed to be University) Hyderabad India Department of CSE School of Technology GITAM (Deemed to Be University) Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist A.P India Department of Computer Science and Engineering RMK College of Engineering and Technology Pudhuvoyal Chennai India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Reliable data on the composition and structure of forests at various spatial scales is necessary for the conservation and monitoring of forest biodiversity. However, because field sampling techniques can be challenging, comprehensive data regarding the features of forest habitat over wide areas are frequently lacking. In order to get over this restriction, we created variables that describe the structure of the forest landscape over a wide environmental gradient using a nationally accessible light detection and ranging (LiDAR) remote sensing dataset. This research proposes a novel technique in forest management based on biodiversity analysis using LiDAR image processing by deep learning techniques. Here, the input LiDAR image has been collected as temperature pattern dataset in major deforestation regions and processed for noise removal and normalization. This image feature has been extracted using recurrent graph learning-based SegNet component analysis. Then, this extracted feature has been classified using Gaussian deep belief region-based adversarial neural networks. The experimental analysis has been carried out for various temperature pattern datasets in terms of random accuracy, average precision, recall, NSE, and AUC. The results of this study will help determine policy objectives for the restoration of mountain areas and enhance the effectiveness of monitoring damaged mountain forests. With the help of the suggested method, 96% random accuracy, 94% average precision, 49% NSE, 93% recall, and 95% AUC were achieved. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial neural networks Deep learning techniques Deforestation rate LiDAR image processing Temperature rise analysis

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A Pattern Classification Model for Vowel data Using Fuzzy Nearest Neighbor

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Intelligent Automation & Soft Computing 2023年第3期35卷 3587-3598页

作者： Monika Khandelwal Ranjeet Kumar Rout Saiyed Umer Kshira Sagar Sahoo NZ Jhanjhi Mohammad Shorfuzzaman Mehedi Masud Department of Computer Science and Engineering National Institute of Technology SrinagarHazratbal190006Jammu and KashmirIndia Department of Computer Science and Engineering Aliah UniversityKolkataIndia Department of Computer Science and Engineering SRM UniversityAmaravati522240APIndia School of Computer Science SCS Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

关键词： Nearest neighbors fuzzy classification patterns recognition reasoning rule membership matrix

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Brain Stroke Prediction Using Random Forest Method with Tuning Parameter 4

Brain Stroke Prediction Using Random Forest Method with Tuni...

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

作者： Felice, Nicole Johan, Jefferson Natthannael, Jevent Gozal, Michael Baptista Jovannie, Charlene Anggreainy, Maria Susan School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Brain stroke is a disease that contributes to the number of deaths in Indonesia. The number of victims of brain stroke is still increasing to this day, so a solution is needed that can predict brain stroke attacks in patients. This research is useful so that patients can be given appropriate medical treatment to prevent brain stroke. To predict brain stroke in patients, researchers designed an application that utilizes machine learning to provide accurate predictions based on several variables. The researcher used a dataset consisting of 4981 patient data with 10 non-labeled attributes who had/not had a brain stroke. By using random forest algorithm which then performed parameter tuning, researchers managed to get an accuracy of 98.98% in predicting the occurrence of brain stroke in patients. © 2023 IEEE.

关键词： Forecasting

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GraphTrials: Visual Proofs of Graph Properties 32

GraphTrials: Visual Proofs of Graph Properties

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32nd International Symposium on Graph Drawing and Network Visualization, GD 2024

作者： Förster, Henry Klesen, Felix Dwyer, Tim Eades, Peter Hong, Seok-Hee Kobourov, Stephen G. Liotta, Giuseppe Misue, Kazuo Montecchiani, Fabrizio Pastukhov, Alexander Schreiber, Falk Department of Computer Science University of Tübingen Germany Institute of Computer Science University of Würzburg Germany Faculty of Information Technology Monash University Australia School of Computer Science The University of Sydney Australia Department of Computer Science University of Arizona TucsonAZ United States Department of Engineering University of Perugia Italy Department of Computer Science University of Tsukuba Japan Department of Psychology University of Bamberg Germany Department of Computer Science University of Konstanz Germany

ISBN: (纸本)9783959773430

Graph and network visualization supports exploration, analysis and communication of relational data arising in many domains: from biological and social networks, to transportation and powergrid systems. With the arrival of AI-based question-answering tools, issues of trustworthiness and explainability of generated answers motivate a greater role for visualization. In the context of graphs, we see the need for visualizations that can convince a critical audience that an assertion about the graph under analysis is valid. The requirements for such representations that convey precisely one specific graph property are quite different from standard network visualization criteria which optimize general aesthetics and readability. In this paper, we aim to provide a comprehensive introduction to visual proofs of graph properties and a foundation for further research in the area. We present a framework that defines what it means to visually prove a graph property. In the process, we introduce the notion of a visual certificate, that is, a specialized faithful graph visualization that leverages the viewer's perception, in particular, pre-attentive processing (e. g. via pop-out effects), to verify a given assertion about the represented graph. We also discuss the relationships between visual complexity, cognitive load and complexity theory, and propose a classification based on visual proof complexity. Finally, we provide examples of visual certificates for problems in different visual proof complexity classes. © Henry Förster, Felix Klesen, Tim Dwyer, Peter Eades, Seok-Hee Hong, Stephen G. Kobourov, Giuseppe Liotta, Kazuo Misue, Fabrizio Montecchiani, Alexander Pastukhov, and Falk Schreiber.

关键词： data visualization

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Comparing Local Binary Pattern and Gray Level Co-occurrence Matrix for Feature Extraction in Diabetic Retinopathy Classification 8

Comparing Local Binary Pattern and Gray Level Co-occurrence ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Makmur, Nathanael Matthew Kwan, Felicia Rana, Astrid Dewi Kurniadi, Felix Indra Computer Science Department Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia

Diabetic retinopathy, a condition characterized by retinal damage and vision loss, is a prevalent complication of diabetes arising from elevated blood sugar levels. With a growing number of individuals affected, efficient and accurate diagnosis is crucial. This study aims to implement and compare the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) feature extraction techniques, which have demonstrated success in prior research. The comparison will provide a comprehensive understanding of the image features, extract relevant data, and improve the performance of the image analysis pipeline for diabetic retinopathy classification. The result showed that from three scenarios the best accuracy provided by Support Vector Machine with the accuracy score between 73% until 74%, however, other algorithm have little difference which the result on 73%. © 2023 The Authors. Published by Elsevier B.V.

关键词： computer Vision Diabetic Retinophaty GLCM LBP

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