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arXiv 2024年

作者： Agrawal, Akanksha Lokshtanov, Daniel Panolan, Fahad Saurabh, Saket Verma, Shaily Department of Computer Science and Engineering Indian Institute of Technology Madras India Department of Computer Science University of California Santa Barbara United States School of Computing University of Leeds United Kingdom Theoretical Computer Science The Institute of Mathematical Sciences HBNI India Department of Informatics University of Bergen Norway Algorithm Engineering Group Hasso Plattner Institute Germany

The classic greedy coloring (first-fit) algorithm considers the vertices of an input graph G in a given order and assigns the first available color to each vertex v in G. In the Grundy Coloring problem, the task is to find an ordering of the vertices that will force the greedy algorithm to use as many colors as possible. In the Partial Grundy Coloring, the task is also to color the graph using as many colors as possible. This time, however, we may select both the ordering in which the vertices are considered and which color to assign the vertex. The only constraint is that the color assigned to a vertex v is a color previously used for another vertex if such a color is available. Partial Grundy coloring of a graph corresponds to the vertex coloring produced by the greedy coloring heuristics called last-fit coloring, which assigns the last available color to each vertex in the given order. Whether Grundy Coloring and Partial Grundy Coloring admit fixed-parameter tractable (FPT) algorithms, algorithms with running time f(k)nO(1), where k is the number of colors, was posed as an open problem by Zaker and by Effantin et al., respectively. Recently, Aboulker et al. (STACS 2020 and Algorithmica 2022) resolved the question for Grundy Coloring in the negative by showing that the problem is W[1]-hard. For Partial Grundy Coloring, they obtain an FPT algorithm on graphs that do not contain Ki,j as a subgraph (a.k.a. Ki,j-free graphs). Aboulker et al. re-iterate the question of whether there exists an FPT algorithm for Partial Grundy Coloring on general graphs and also asks whether Grundy Coloring admits an FPT algorithm on Ki,j-free graphs. We give FPT algorithms for Partial Grundy Coloring on general graphs and for Grundy Coloring on Ki,j-free graphs, resolving both the questions in the affirmative. We believe that our new structural theorems for partial Grundy coloring and "representative-family" like sets for Ki,j-free graphs that we use in obtaining our results may have wi

关键词： Graph algorithms

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Best-Answer Prediction in Q&A Sites Using User Information

arXiv

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arXiv 2022年

作者： Hadfi, Rafik Moustafa, Ahmed Yoshino, Kai Ito, Takayuki Department of Social Informatics Kyoto University Japan Department of Computer Science Nagoya Institute of Technology Japan

Community Question Answering (CQA) sites have spread and multiplied significantly in recent years. Sites like Reddit, Quora, and Stack Exchange are becoming popular amongst people interested in finding answers to diverse questions. One practical way of finding such answers is automatically predicting the best candidate given existing answers and comments. Many studies were conducted on answer prediction in CQA but with limited focus on using the background information of the questionnaires. We address this limitation using a novel method for predicting the best answers using the questioner’s background information and other features, such as the textual content or the relationships with other participants. Our answer classification model was trained using the Stack Exchange dataset and validated using the Area Under the Curve (AUC) metric. The experimental results show that the proposed method complements previous methods by pointing out the importance of the relationships between users, particularly throughout the level of involvement in different communities on Stack Exchange. Furthermore, we point out that there is little overlap between user-relation information and the information represented by the shallow text features and the meta-features, such as time differences. © 2022, CC BY.

关键词： Forecasting

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Automated Indonesian Essay Scoring and Holistic Feedback Using Bidirectional Encoder Representations for Transformers

Automated Indonesian Essay Scoring and Holistic Feedback Usi...

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International Conference on Electrical, Telecommunication and computer Engineering (ELTICOM)

作者： Amalia Amalia Maya Silvi Lydia Rabiah Abdul Kadir Faradhila Aulia Utami Tanjung Dewi Sartika Br Ginting Dani Gunawan Department of Computer Science Universitas Sumatera Utara Medan Indonesia Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Malaysia Department of Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331531249

ISBN: (纸本)9798331531256

Developing an effective and accurate Automated Essay Scoring (AES) system for Indonesian essays involves overcoming several challenges, including limited datasets and issues related to grading efficiency and feedback generation. This study addresses these challenges by proposing an automated Indonesian essay grading and feedback system utilizing Bidirectional Encoder Representations from Transformers (BERT). The research employed a thorough process, including the translation of the ASAP dataset from the Kaggle competition titled “The Hewlett Foundation” into Indonesian, analysis and refinement of the dataset to include only relevant features, and fine-tuning using the pre-trained IndoBERT embeddings. To assess the relevance between the essay prompts and student responses, text similarity methods were applied outside the fine-tuning process. The final score is derived from the combination of the fine-tuned score and the relevance score. The model achieved a perfect accuracy of 1 and a strong kappa score of 0.82 during training. The alignment of the final score predictions with human feedback is validated by a Quadratic Weighted Kappa (QWK) score of 0.9, indicating a high level of agreement. This demonstrates that the automated scoring system effectively matches human judgment, highlighting its efficacy in evaluating essay quality.

关键词： Training Electric potential Accuracy Translation Computational modeling Bidirectional control Transformers Encoding Communications technology Testing

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THREE MECHANISMS OF FEATURE LEARNING IN A LINEAR NETWORK

arXiv

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arXiv 2024年

作者： Xu, Yizhou Ziyin, Liu Computer and Communication Sciences École Polytechnique Fédérale de Lausanne Switzerland Research Laboratory of Electronics Massachusetts Institute of Technology United States Physics & Informatics Laboratories NTT Research

Understanding the dynamics of neural networks in different width regimes is crucial for improving their training and performance. We present an exact solution for the learning dynamics of a one-hidden-layer linear network, with one-dimensional data, across any finite width, uniquely exhibiting both kernel and feature learning phases. This study marks a technical advancement by enabling the analysis of the training trajectory from any initialization and a detailed phase diagram under varying common hyperparameters such as width, layer-wise learning rates, and scales of output and initialization. We identify three novel prototype mechanisms specific to the feature learning regime: (1) learning by alignment, (2) learning by disalignment, and (3) learning by rescaling, which contrast starkly with the dynamics observed in the kernel regime. Our theoretical findings are substantiated with empirical evidence showing that these mechanisms also manifest in deep nonlinear networks handling real-world tasks, enhancing our understanding of neural network training dynamics and guiding the design of more effective learning strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer:a comprehensive comparative study

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Intelligent Medicine 2024年第2期4卷 114-127页

作者： Ao Chen Chen Li Md Mamunur Rahaman Yudong Yao Haoyuan Chen Hechen Yang Peng Zhao Weiming Hu Wanli Liu Shuojia Zou Ning Xu Marcin Grzegorzek Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information EngineeringNortheastern UniversityShenyangLiaoning 110169China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA School of Arts and Design Liaoning Petrochemical UniversityFushunLiaoning 113001China Institute of Medical Informatics University of LübeckLüebeckGermany

Background With the gradual increase of infertility in the world,among which male sperm problems are the main factor for infertility,more and more couples are using computer-assisted sperm analysis(CASA)to assist in the analysis and treatment of ***,the rapid development of deep learning(DL)has led to strong results in image classification ***,the classification of sperm images has not been well studied in current deep learning methods,and the sperm images are often affected by noise in practical CASA *** purpose of this article is to investigate the anti-noise robustness of deep learning classification methods applied on sperm *** The SVIA dataset is a publicly available large-scale sperm dataset containing three *** this work,we used subset-C,which provides more than 125,000 independent images of sperms and impurities,including 121,401 sperm images and 4,479 impurity *** investigate the anti-noise robustness of deep learning classification methods applied on sperm images,we conducted a comprehensive comparative study of sperm images using many convolutional neural network(CNN)and visual transformer(VT)deep learning methods to find the deep learning model with the most stable anti-noise *** This study proved that VT had strong robustness for the classification of tiny object(sperm and impurity)image datasets under some types of conventional noise and some adversarial *** particular,under the influence of Poisson noise,accuracy changed from 91.45%to 91.08%,impurity precison changed from 92.7%to 91.3%,impurity recall changed from 88.8%to 89.5%,and impurity F1-score changed 90.7%to 90.4%.Meanwhile,sperm precision changed from 90.9%to 90.5%,sperm recall changed from 92.5%to 93.8%,and sperm F1-score changed from 92.1%to 90.4%.Conclusion Sperm image classification may be strongly affected by noise in current deep learning methods;the robustness with regard to noise of VT methods based

关键词： computer-assisted sperm analysis Anti-noise Robustness Deep learning .Image classification Sperm image Conventional noise Adversarial attacks Convolutional neural network Visual transformer

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Non-Destructive Egg Fertility Detection in Incubation Using SVM Classifier Based on GLCM Parameters

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Procedia computer Science 2022年 207卷 3254-3263页

作者： Shofan Saifullah Rafał Dreżewski Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta Indonesia Institute of Computer Science AGH University of Science and Technology Cracow Poland

The research presented in this paper aims at egg fertility detection during incubation based on the presence of the embryo. To obtain an optimal detection method that does not adversely affect the incubation process, the presented approach uses computer technology to analyze the images obtained from the egg candling process, and to obtain information about the presence of an egg embryo. This study applies the Support Vector Machine (SVM) classifier method using the second-order statistical feature extraction input to detect the chicken egg's fertility. The feature extraction is based on the Gray-Level Co-occurrence Matrix (GLCM) approach with 6 parameters: Energy (En), Contrast (Ct), Entropy (Et), Variance (V), Correlation (Cr), and Homogeneity (H). This study develops a manual detection process that takes a long time and improves the research accuracy of the backpropagation method. The dataset used consisted of 100 images of chicken eggs, including 50 images for each type: fertile and infertile. The preprocessing of the images included cropping, grayscaling, and image enhancement using CLAHE (Contrast Limited Adaptive Histogram Equalization) and HE (Histogram Equalization) methods. Such process can improve the image to get its GLCM parameters. The extracted GLCM parameters are input to the egg fertility detection process performed by the SVM classifier-based method. The presented SVM-GLCM approach was able to detect fertile and infertile eggs with a success rate of 98.20%, which was an improvement over the previous research. This research can be a reference for implementing the detection of chicken egg fertility in the incubation machine to obtain optimal hatching results.

关键词： SVM classifier GLCM image processing egg fertility detection

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Assessment of Driver’s Stress using Multimodal Biosignals and Regularized Deep Kernel Learning

Assessment of Driver’s Stress using Multimodal Biosignals a...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vishal Singh Roha Nagarajan Ganapathy Nicolai Spicher Sriparna Saha Thomas M. Deserno Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School Brunswick Germany Department of Computer Science Engineering Indian Institute of Technology Patna India Department of Biomedical Engineering Indian Institute of Technology Hyderabad India Institute for Medical Informatics University Medical Center Göttingen Göttingen Germany

In this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and respiration rate (RESP) from N = 10 healthy drivers in experiments of 25min duration with different stress states (5min resting, 10min driving, 10min driving + answering cognitive questions). We manually remove unusable segments and approximately 4h of data remain. Multimodal time and frequency features are extracted and employed to regularized deep kernel machine learning based on a fusion framework. Task-specific representations of different physiological signals are combined using intermediate fusion. Subsequently, the fused multimodal features are fed a support vector machine (SVM) and a random forest (RF) for stress classification. The experimental results show that the proposed approach can discriminate between stress states. The combination of PPG and ECG using RF as classifier yields the highest F1-score of 0.97 in the test set. PPG only and RF yield a maximum F1-score of 0.90. Furthermore, subject-specific cross-validation improves performance. ECG and PPG signals are reliable in classifying the stress state of a car driver. In summary, the proposed framework could be extended to real-time stress state assessment in driving conditions.

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Naming Scheme on Named Data Networking: A Survey 8

Naming Scheme on Named Data Networking: A Survey

引用

8th International Conference on Wireless and Telematics, ICWT 2022

作者： Nurhayati, Ade Mayasari, Ratna Ahdan, Syaiful Negara, Ridha Muldina Nurkahfi, Galih Nugraha Syambas, Nana Rachmana Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung40132 Indonesia Faculty of Engineering Institut Teknologi Telkom Jakarta Jakarta11710 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Lampung35132 Indonesia

ISBN: (数字)9781665451222

ISBN: (纸本)9781665451222

Named Data Networking (NDN) has been introduced as the future internet architecture adopting a new network architecture that impacts changes in the applications developed. NDN brings about the transition from host-centric communication to information-centric communication, where data are masterpieces. The process of retrieving and requesting data on ndn by utilizing the naming scheme. The naming scheme will affect how long it takes to find and obtain the desired data. There are several basic naming schemes, such as flat naming, hierarchical naming, and hybrid naming, where each has a different performance. In addition, there are also several schemes have been modified to optimize the existing scheme such as entropy approach, name component encoding, and smart name lookup. © 2022 IEEE.

关键词： Network architecture

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Security Revisited: Honeytokens meet Google Authenticator 7

Security Revisited: Honeytokens meet Google Authenticator

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7th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Papaspirou, Vassilis Papathanasaki, Maria Maglaras, Leandros Kantzavelou, Ioanna Douligeris, Christos Ferrag, Mohamed Amine Janicke, Helge University of West Attica Department of Informatics and Computer Engineering Athens Greece University of Thessaly Department of Cs and Telecommunications Lamia Greece Cyber Technology Institute De Montfort University Leicester United Kingdom University of Piraeus Department of Informatics Piraeus Greece Guelma University Department of Computer Science Guelma Algeria Cyber Security CRC Edith Cowan University Perth Perth6027 Australia

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

In contrast to the rapid advancement and evolution of technology, computer systems rely significantly on passwords to maintain their security for an unreasonably long time. Although the introduction of two or more factors improved existing authentication procedures and gave rise to new multi-factor authentication schemes, more advanced and focused assaults have revealed they are also sensitive and ineffective to assure only authorized access to systems. This study suggests a revolutionary two-factor authentication mechanism that uses QR codes and honeytokens. In the current implementation, honeywords and Google authenticator work together. In order to speed up the identification process, the honeywords technique is utilized as a second factor on a different computing device. By using a stronger and more effective authentication mechanism than a single password mechanism or a common two-factor authentication scheme, the novel and straightforward approach presented here intends to add additional layers of security and protection into a system, thereby increasing its security. © 2022 IEEE.

关键词： Authentication

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Mobility-Aware Multi-Task Decentralized Federated Learning for Vehicular Networks: Modeling, Analysis, and Optimization

arXiv

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arXiv 2025年

作者： Chen, Dongyu Deng, Tao Huang, He Jia, Juncheng Dong, Mianxiong Yuan, Di Li, Keqin School of Computer Science and Technology Soochow University Jiangsu Suzhou215006 China Department of Sciences and Informatics Muroran Institute of Technology Muroran050-8585 Japan Department of Information Technology Uppsala University Uppsala751 05 Sweden Department of Computer Science State University of New York New PaltzNY12561 United States

Federated learning (FL) is a promising paradigm that can enable collaborative model training between vehicles while protecting data privacy, thereby significantly improving the performance of intelligent transportation systems (ITSs). In vehicular networks, due to mobility, resource constraints, and the concurrent execution of multiple training tasks, how to allocate limited resources effectively to achieve optimal model training of multiple tasks is an extremely challenging issue. In this paper, we propose a mobility-aware multi-task decentralized federated learning (MMFL) framework for vehicular networks. By this framework, we address task scheduling, subcarrier allocation, and leader selection, as a joint optimization problem, termed as TSLP. For the case with a single FL task, we derive the convergence bound of model training. For general cases, we first model TSLP as a resource allocation game, and prove the existence of a Nash equilibrium (NE). Then, based on this proof, we reformulate the game as a decentralized partially observable Markov decision process (DEC-POMDP), and develop an algorithm based on heterogeneous-agent proximal policy optimization (HAPPO) to solve DEC-POMDP. Finally, numerical results are used to demonstrate the effectiveness of the proposed algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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