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Consumer Communications and Networking Conference, CCNC IEEE

作者： Ren Goto Kazushige Matama Ryouta Aihata Michiyo Suda Hidekazu Suzuki Katsuhiro Naito Graduate School of Business Administration and Computer Science Aichi Institute of Technology Toyota Aichi Japan Faculty of Information Science Aichi Institute of Technology Toyota Aichi Japan Faculty of Information Engineering Meijo University Nagoya Aichi Japan

Peer-to-Peer (P2P) communication emerges as a potent paradigm in distributed processing models. However, network accessibility challenges and inherent limitations in ex-isting solutions have impeded the full realization of P2P's poten-tial. To address these issues, we've developed CYber PHysical Overlay Network over Internet Communication (CYPHONIC), an innovative solution enabling P2P-based direct device-to-device communication while ensuring robust zero-trust security. Additionally, we introduce the concept of a gateway device equipped with a CYPHONIC adapter to cater to conventional devices, often referred to as general nodes. Our paper focuses on implementing a remote camera monitoring solution, providing secure access to surveillance cameras. We aim to demonstrate that our overlay network seamlessly connects all CYPHONIC clients, including general nodes. We also present a comprehensive case study centered on remote camera monitoring. This case study showcases the effortless connectivity enabled by our overlay network, highlighting how CYPHONIC clients, including general nodes, can efficiently interconnect.

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Enhancing Taiwanese Hokkien Dual Translation by Exploring and Standardizing of Four Writing Systems

arXiv

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arXiv 2024年

作者： Lu, Bo-Han Lin, Yi-Hsuan Lee, En-Shiun Annie Tsai, Richard Tzong-Han Department of Computer Science and Information Engineering National Central University Taiwan Department of Computer Science Faculty of Arts and Science University of Toronto Canada Computer Science Program Faculty of Science Ontario Tech University Canada Center for GIS Research Center for Humanities and Social Sciences Academia Sinica Taiwan

Machine translation focuses mainly on high-resource languages (HRLs), while low-resource languages (LRLs) like Taiwanese Hokkien are relatively under-explored. The study aims to address this gap by developing a dual translation model between Taiwanese Hokkien and both Traditional Mandarin Chinese and English. We employ a pre-trained LLaMA 2-7B model specialized in Traditional Mandarin Chinese to leverage the orthographic similarities between Taiwanese Hokkien Han and Traditional Mandarin Chinese. Our comprehensive experiments involve translation tasks across various writing systems of Taiwanese Hokkien as well as between Taiwanese Hokkien and other HRLs. We find that the use of a limited monolingual corpus still further improves the model’s Taiwanese Hokkien capabilities. We then utilize our translation model to standardize all Taiwanese Hokkien writing systems into Hokkien Han, resulting in further performance improvements. Additionally, we introduce an evaluation method incorporating back-translation and GPT-4 to ensure reliable translation quality assessment even for LRLs. The study contributes to narrowing the resource gap for Taiwanese Hokkien and empirically investigates the advantages and limitations of pre-training and fine-tuning based on LLaMA 2. © 2024, CC BY-NC-SA.

关键词： Neural machine translation

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Comparison of DenseNet201, InceptionV3, and Their Fusion Models in Breast Cancer Tissue Image Classification 16

Comparison of DenseNet201, InceptionV3, and Their Fusion Mod...

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16th International Conference on Digital Image Processing, ICDIP 2024

作者： Ge, Ruigang Chen, Guoyue Saruta, Kazuki Terata, Yuki Graduate School of Akita Prefectural University 84-4 Ebinokuchi Tsuchiya Yurihonjo city Akita015-0011 Japan Dept. of Information and Computer Science Faculty of Systems and Technology Akita Prefectural University 84-4 Ebinokuchi Tsuchiya Yurihonjo city Akita015-0011 Japan

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

In this study, we propose a fusion model based on DenseNet201 and InceptionV3 aimed at improving the accuracy of 8-class classification of the BreakHis breast cancer histopathological image dataset. The BreakHis dataset poses significant challenges for automatic classification tasks due to its high feature similarity and class imbalance. Our model leverages the deep connectivity of DenseNet201 and the broad exploratory capabilities of InceptionV3 to capture comprehensive features from local to global scales, enhancing adaptability to various feature scales. This fusion model outperforms individual DenseNet201 or InceptionV3 models in key performance metrics such as precision, recall, and F1 score. It shows marked performance improvements, particularly in categories with high feature similarity and fewer samples, demonstrating its effectiveness in addressing internal dataset imbalances. Additionally, the model exhibits stability across multiple training and testing iterations, further validating its reliability and effectiveness in classifying breast cancer histopathological images. The results indicate that the fusion of DenseNet201 and InceptionV3 models can enhance the accuracy of automatic classification of breast cancer histopathological images, especially in categories with high feature similarity. This finding provides valuable insights for the development and selection of classification models and contributes to future work in medical image analysis. © 2024 SPIE.

关键词： Lung cancer

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Machine Speech Chain with Emotion Recognition

Machine Speech Chain with Emotion Recognition

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Akeyla Pradia Naufal Dessi Puji Lestari Ayu Purwarianti Kurniawati Azizah Dipta Tanaya Sakriani Sakti School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia Graduate School of Information Science Nara Institute of Science and Technology Nara Japan

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Developing natural speech recognition and speech synthesis systems requires speech data that authentically represents real emotions. However, this type of data is often challenging to obtain. Machine speech chain offers a solution to this challenge by using unpaired data to continue training models initially trained with paired data. Given the relative abundance of unpaired data compared to paired data, machine speech chain can be instrumental in recognizing emotions in speech where training data is limited. This study investigates the application of machine speech chain in speech emotion recognition and speech recognition of emotional speech. Our findings indicate that a model trained with 50% paired neutral emotion speech data and 22% paired non-neutral emotional speech data shows a reduction in Character Error Rate (CER) from 37.55% to 34.52% when further trained with unpaired neutral emotion speech data. The CER further decreases to 33.75% when additionally trained with combined unpaired speech data. The accuracy of recognizing non-neutral emotions ranged from 2.18% to 53.51 %, though the F1 score fluctuated, increasing by up to 20.6% and decreasing by up to 23.4%. These results suggest that the model demonstrates a bias towards the majority class, as reflected by the values of the two metrics.

关键词： Training Emotion recognition Accuracy Error analysis Natural languages Training data Speech recognition Speech enhancement Data models Speech synthesis

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Solving Multiagent Path Finding on Highly Centralized Networks 39

Solving Multiagent Path Finding on Highly Centralized Networ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fioravantes, Foivos Knop, Dušan Křišťan, Jan Matyáš Melissinos, Nikolaos Opler, Michal Vu, Tung Anh Department of Theoretical Computer Science Faculty of Information Technology Czech Technical University in Prague Prague Czech Republic Computer Science Institute Faculty of Mathematics and Physics Charles University Prague Czech Republic

ISBN: (纸本)157735897X

The MUTLIAGENT PATH FINDING (MAPF) problem consists of identifying the trajectories that a set of agents should follow inside a given network in order to reach their desired destinations as soon as possible, but without colliding with each other. We aim to minimize the maximum time any agent takes to reach their goal, ensuring optimal path length. In this work, we complement a recent thread of results that aim to systematically study the algorithmic behavior of this problem, through the parameterized complexity point of view. First, we show that MAPF is NP-hard when the given network has a star-like topology (bounded vertex cover number) or is a tree with 11 leaves. Both of these results fill important gaps in our understanding of the tractability of this problem that were left untreated in the recent work of Fioravantes et al., Exact Algorithms and Lowerbounds for Multiagent Path Finding: Power of Treelike Topology, presented in AAAI’24. Nevertheless, our main contribution is an exact algorithm that scales well as the input grows (FPT) when the topology of the given network is highly centralized (bounded distance to clique). This parameter is significant as it mirrors real-world networks. In such environments, a bunch of central hubs (e.g., processing areas) are connected to only few peripheral nodes. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Preferences for Using Canva Apps as a Presentation Media Creation Tool Among Gen Z Students: SEM Analysis with TAM and UTAUT2 Approaches 25

Preferences for Using Canva Apps as a Presentation Media Cre...

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25th International Seminar on Intelligent technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Purnomo, Agung Arifuddin, Rahman Widartha, Vandha Pradwiyasma Adi, Puput Dani Prasetyo Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Medicine Airlangga Surabaya60132 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia Research Center for Telecommunication Bandung Indonesia

ISBN: (纸本)9798350378573

The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "technology Acceptance Model (TAM)"and the "Unified Theory of Acceptance and Use of technology (UTAUT2)". "Structural Equation Modeling (SEM)"used for data analyzation. This research aims to understand the dynamics of Canva acceptance among students. The findings show that enjoyment, fun, and pleasure in using Canva, combined with its creative tools and design-centric approach, significantly resonate with Gen Z's preference for aesthetics. This research highlights that Canva is favored by students for creating engaging presentations. © 2024 IEEE.

关键词： Students

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An Improved Deep Learning Framework Based on Multi-Scale Convolutional Architecture for Road Crack Detection 2nd

An Improved Deep Learning Framework Based on Multi-Scale Con...

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2nd International Conference on Data science and Network Engineering, ICDSNE 2024

作者： Idris, Idris Ya’u Ya’u, Badamasi Imam Ali, Usman Gulzar, Yonis Department of Computer Science School of Science and Technology Federal Polytechnic Bauchi Bauchi743001 Nigeria Department of Mathematical Sciences Faculty of Science Abubakar Tafawa Balewa University Bauchi740272 Nigeria Gombe760253 Nigeria Department of Management Information Systems College of Business Administration King Faisal University Al Ahsa31982 Saudi Arabia

ISBN: (纸本)9789819783359

In addition to serving as a means of transportation, a highway connects the local economy. But as the pavement ages, several flaws including cracks, potholes, and deformation gradually show up on the surfaces of the road. Crack evaluation is typically done by hand using field surveys performed by humans. Nevertheless, these labor-intensive, time-consuming, manual survey methods are not reproducible or repeatable and place surveyors in dangerous situations. This proposes a novel deep-learning framework for road crack detection based on multi-scale convolutional architecture. To address the challenges of object occlusion, soft non-maximal suppression (NMS) is applied across crack proposals at various feature scales. The experimental results show that the introduced model attains an accuracy of 0.9989 and better detection performance across four distinct object proposal building block (OPBB) architectures. Thus, our model outperforms the benchmark models in every instance of OPBBs. In this way, the problem of large object scale variation in road crack images with different object occlusion challenges has been tackled, and the positive images are divided into a set of semantically significant regions, such as the road surface and cracks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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An efficient approach for congestion detection and control in VANETs

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International Journal of Ad Hoc and Ubiquitous Computing 2025年第2期49卷 105-118页

作者： Chaurasia, Brijesh Kumar Rishiwal, Vinay Shiblee, Mohammad Shukla, Man Mohan Yadav, Mano Department of Computer Science and Engineering Pranveer Singh Institute of Technology UP Kanpur India Department of Computer Science and Information Technology Faculty of Engineering and Technology MJP Rohilkhand University UP Bareilly India Industrial Engineering Department Taif University Taif21944 Saudi Arabia Pranveer Singh Institute of Technology UP Kanpur India Department of Computer Science Bareilly College UP Bareilly India

Vehicular ad hoc network (VANET) is a promising technology that uses a variety of messages to deliver convenience and safety features. VANET has unique characteristics regarding the high speed and variable density of vehicles. The short time for vehicular communication, especially at high speeds, presents difficulties. Channel utilisation, especially for safety messages, is a big issue due to its unique characteristics, which can lead to congestion, higher delays, and energy waste. This problem affects the performance of the VANET as a whole and the efficiency of VANET applications, such as the emergency notification system. This work aims to design a novel mechanism for managing and detecting congestion in VANET to mitigate these issues. The proposed mechanism uses priority-based channel assignment to handle congestion control in VANET. The extensive simulation results demonstrate that the proposed approach effectively addresses congestion, ensuring a more reliable environment for VANET operations. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Signaling

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A comparative study of augmented inverse propensity weighted estimators using outcome-oriented covariate selection via penalization with outcome-adaptive lasso

arXiv

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arXiv 2024年

作者： Hongo, Wataru Ando, Shuji Tsuchida, Jun Sozu, Takashi Department of Information and Computer Technology Graduate School of Engineering Tokyo University of Science Tokyo Japan Advanced Quantitative Sciences Development Novartis Pharma K.K. Tokyo Japan Department of Information Sciences Faculty of Science and Technology Tokyo University of Science Chiba Japan Department of Data Science Faculty of Data Science Kyoto Women’s University Kyoto Japan Department of Information and Computer Technology Faculty of Engineering Tokyo University of Science Tokyo Japan

Background: When estimating causal effects from observational data with numerous covariates, employing penalized covariate selection can improve the estimation efficiency. Outcome-oriented covariate selection, which involves selecting covariates related to the outcome, can enhance efficiency, even for propensity score (PS) methods. For outcome-oriented covariate selection in PS models, outcome-adaptive lasso can be used for penalization with the oracle property. The performance of inverse propensity weighted (IPW) estimators using outcome-adaptive lasso was shown to be superior to that of the IPW estimators using other covariate selection methods for parametric models. However, the augmented IPW (AIPW) estimator is typically employed as a doubly robust estimator for the average treatment effect, which requires both PS and outcome models. Despite this, which covariate selection method for outcome models should be combined with the outcome-adaptive lasso to form the AIPW estimator remains unclear. We evaluated the performance of the AIPW estimators using the outcome-adaptive lasso for PS models and various outcome-oriented covariate selection via penalization for outcome models. Methods: We conducted numerical experiments to evaluate the performance of AIPW estimators using various covariate selection via penalization. In numerical experiments, we assessed bias, standard error, and root-mean-squared error. We applied the AIPW estimators to a clinical trial dataset for reference, comparing their point estimates and standard errors obtained via bootstrap. Results: In the numerical experiments, the performance of the AIPW estimators using outcome-oriented covariate selection via penalization with the oracle property for both PS and outcome models was superior to that of the other estimators and similar to that of the AIPW estimator, which relies on true confounders and outcome predictors. In contrast, the bias of the AIPW estimators not relying on the oracle property was

关键词： Mean square error

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Diabetes Classification Using Support Vector Machine: Binary Classification Model 4

Diabetes Classification Using Support Vector Machine: Binary...

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4th International Conference on information and Communications technology, ICOIACT 2021

作者： Rabiha, Suciana Ghadati Wibowo, Antoni Lukas Heryadi, Yaya Computer Science Department BINUS Graduate Program-Doctor of Computer Science. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University 11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University 11480 Indonesia

ISBN: (纸本)9781665433945

One of the health problems that require special attention is diabetes, besides the growth of this disease infection is increasing in various circles ranging from children, adults, men, women and the elderly. So to detect diabetes at an early stage, machine learning algorithms are the right choice to help detect indications of diabetes. This paper presents experimental results related to the exploration of the use of a Super Vector Machine (SVM) for data classification of Diabetes. The model used in this experiment is the Binary Classification Model. Based on the test results, the highest accuracy is obtained by using a total of 700 lines of data, which is 75,7143%. The Best estimated feasible point in Box Constraint is 272.29 with a KernelScale area of 72.627. © 2021 IEEE

关键词： Face recognition

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