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Deciphering DNA variant-associated aberrant splicing with the aid of RNA sequencing (vol 55, pg 732, 2023)

NATURE GENETICS 2023年第7期55卷 1251-1251页

作者： Zhang, Bin Gao, Xin Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia KAUST Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia

Aberrant RNA splicing events resulting from DNA variations are common causes of genetic disorders. Two studies published in Nature Genetics independently describe methods to decipher DNA-variant-associated aberrant sp... 详细信息

关键词： Data mining High-throughput screening

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QoE Comparison between Multi-User OFDMA and Reliable Groupcast for Wireless LAN Audiovisual Transmission

QoE Comparison between Multi-User OFDMA and Reliable Groupca...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Deguchi, Daiki Nunome, Toshiro Graduate School of Engineering Nagoya Institute of Technology Computer Science Program Department of Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350364866

This paper compares video and audio QoE of OFDMA multi-user transmission and reliable groupcast over wireless LAN. We assume video and audio transmission to several terminals simultaneously. As a reliable groupcast method, we employ Unsolicited Retry, a technique of IEEE 802.11aa GroupCast with Retries. We utilize IEEE 802.11ax for OFDMA transmission. We evaluate application-level QoS by computer simulation. We then assess QoE through a subjective experiment with video and audio streams generated by the output timing obtained from the simulation. We notice that each method has a situation to be applied appropriately. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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A model of service-oriented architecture of e-certification system to support boat registration and site visit inspection to support maritime safety and crew health inspection

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Marine Systems and Ocean technology 2023年第1-2期18卷 45-63页

作者： Saputra, Diaz Gaol, Ford Lumban Abdurachman, Edi Sensuse, Dana Indra Matsuo, Tokuro Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Binus Graduate Program - Master in Information System Bina Nusantara University Jakarta11480 Indonesia Department of Research in Management Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan City University of Macau China Asia University Taichung Taiwan

This article describes the architecture of software services that provide registration of small boat data to build maritime safety. The proposed system architecture which so-called SIMKAPEL aims to support small boat registration for maritime safety. It is developed from a conventional service-oriented architecture (SOA) with an open standard for data interoperability between elements. The goal of this research is to implement and evaluate the proposed architecture to support small boat registration and e-certification. Several parts of the architecture have been implemented as a platform that provides core services and data sources to be used by Ministry of Transportation, Directorate General of Sea Transportation, and other government agencies. Finally, the proposed SIMKAPEL platform with the SOA architecture is evaluated for its benefits to users through the expert opinion survey to measure its efficiency. The analysis results showed that SOA-based SIMKAPEL is feasible to use as a boat e-certification system. Furthermore, experiment results provide three parameters (e.g., maritime safety, namely, cargo/passengers, boat safety facilities, and maritime safety systems) with efficiency values of 42%, 46%, and 42%, respectively. The parameter with the highest efficiency shows a big effect on the level of safety of the boat and passengers. Thus, the parameters that need to be prioritized and need boat owners’ attention are cargo/passenger, boat safety equipment, and maritime safety system. © 2023, The Author(s).

关键词： Boat e-certification Boat online certification Cloud-based service Integrated system SOA architecture

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Threat Modeling for Honeypot Deployment 10

Threat Modeling for Honeypot Deployment

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10th IEEE Information technology International Seminar, ITIS 2024

作者： Silaen, Kalpin Erlangga Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Institute of Technology Bandung Faculty of Digital Communication and Hotel & Tourism Bandung Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798331521295

The increasing complexity of cyber attacks, including 0-day vulnerabilities and APTs, has rendered traditional defenses like firewalls and IDS/IPS insufficient. Honeypots have been proposed as a solution to detect and analyze new attack types by simulating vulnerable systems and capturing malicious activities. However, the deployment of honeypots introduces unique risks, necessitating a comprehensive threat modeling approach to mitigate potential drawbacks. This paper explores the intersection of threat modeling and honeypot deployment, identifying threats, and proposing effective mitigation strategies. We applied a threat model adapted from a simplified PASTA framework based on risk-centric and proposed mitigation plans such as network segmentation, outbound traffic filtering, resource monitoring, and regular updates. Additionally, we discussed alternative honeypot deployments outside the organization's internal network to avoid arising risks. We found that deploying high-interaction honeypots carries high risks due to a broader attack flow. The novelty of this study lies in adapting the attack-flow approach for honeypot threat modeling, providing a structured method to analyze and mitigate honeypot-specific threats. Future research directions include conducting long-term studies and detailed case studies to further optimize the interaction between honeypot deployments and threat modeling for enhanced security outcomes. © 2024 IEEE.

关键词： Zero-day attack

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Leveraging Attention to Achieve Generalization for Image Forgery Detection 1

Leveraging Attention to Achieve Generalization for Image For...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Atta, Mohamed Nile University Artificial Intelligence Program School of Information Technology and Computer Science Giza Egypt

ISBN: (纸本)9798350335569

Recently, image forgery has become an alarming trend with the growth of available easy-to-use editing and generation tools. Modern DeepFake methods have achieved extraordinary progress in realistic face manipulation, thus raising concerns among the public about the misuse of such technologies. Unfortunately, with the obnoxiously wide range of possible manipulation and artifact-covering methods, most existing state-of-the-art detection methods lack the generalization capability to handle the output variations. To address this issue, a noticeable shift towards using attention mechanisms has emerged using balanced portions of the latest challenging datasets to detect intra-and inter-spatial relations. Our paper provides a comprehensive analysis of modern deep learning-based methods, showing the benefits of the shift. In addition, we make propositions for future research directions and dataset-building methodology. © 2023 IEEE.

关键词： Generative adversarial networks

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Feature Learning using Deep Variational Autoencoder for Prediction of Defects in Car Engine

Feature Learning using Deep Variational Autoencoder for Pred...

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2024 International Conference on Information technology Research and Innovation, ICITRI 2024

作者： Susanto, Nanang Pardede, Hilman Ferdinandus Universitas Nusa Mandiri Computer Science Study Program Jakarta Indonesia National Research and Innovation Agency Indonesia Universitas Nusa Mandiri Computer Science Faculty of Information Technology Jakarta Indonesia

ISBN: (纸本)9798350376210

Machine learning has been employed to automatically detect the defects on car engines in several studies. One of crucial challenges on applying machine learning is on the amount of defect data collected is often large with high dimensional data, making manual detection inefficient and inaccurate. The other problem is on the missing data as oftentimes the collected data are incomplete. In this paper, we employ machine learning frameworks for engine defect detection. It comprises the data pre-processing stage which includes imputing missing value data, then performing feature correlation using the Pearson method, and selecting the features to use. After that, the label encoder and standard scaler are carried out. The experimental process begins with creating a baseline, then continues with improving imbalance data using SMOTE, and feature reconstruction using variational autoencoder (VAE). Afterwards, for classification, we employ convolutional neural networks (CNN). The proposed method achieved precision 99.63%. We collect engine quality dataset of 224,239 data with 90 features from major automobile manufacturing in Indonesia. This showed that SMOTE and Variational Autoencoder dimensional reconstruction method can overcome defect predictions in car engine defect data with data imbalance conditions. This novel methodology distinguished our study from prior methods and shows considerable increases in precision and recall matrix. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving graph-based recommendation with unraveled graph learning

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Data Mining and Knowledge Discovery 2024年第4期38卷 2440-2465页

作者： Chang, Chih-Chieh Tzeng, Diing-Ruey Lu, Chia-Hsun Chang, Ming-Yi Shen, Chih-Ya The Management Undergraduate Program National Taiwan University of Science and Technology Taipei City Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Sociology Fu Jen Catholic University New Taipei City Taiwan

Graph Collaborative Filtering (GraphCF) has emerged as a promising approach in recommendation systems, leveraging the inferential power of Graph Neural Networks. Furthermore, the integration of contrastive learning has enhanced the performance of GraphCF methods. Recent research has shifted from graph augmentation to noise perturbation in contrastive learning, leading to significant performance improvements. However, we contend that the primary factor in performance enhancement is not graph augmentation or noise perturbation, but rather the balance of the embedding from each layer in the output embedding. To substantiate our claim, we conducted preliminary experiments with multiple state-of-the-art GraphCF methods. Based on our observations and insights, we propose a novel approach named Unraveled Graph Contrastive Learning (UGCL), which includes a new propagation scheme to further enhance performance. To the best of our knowledge, this is the first approach that specifically addresses the balance factor in the output embedding for performance improvement. We have carried out extensive experiments on multiple large-scale benchmark datasets to evaluate the effectiveness of our proposed approach. The results indicate that UGCL significantly outperforms all other state-of-the-art baseline models, also showing superior performance in terms of fairness and debiasing capabilities compared to other baselines. © The Author(s), under exclusive licence to Springer science+Business Media LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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Boost DC-DC Converter with Non-dissipative Snubber

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IEEJ Transactions on Electrical and Electronic Engineering 2024年第11期19卷 1858-1878页

作者： Yau, Yeu-Torng Department of Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology Taichung No.57 Sec. 2 Zhongshan Rd. Taiping Dist Taichung41170 Taiwan

To provide a hold-up time function in DC-DC supplies for cell site stations or data centers, using a boost converter with a bulk output capacitor as a front-end converter stage is a simple and highly cost-effective solution. However, conventional boost converters with a hard-switching operation result in low conversion efficiency and increased electromagnetic interference emissions. In this paper, a boost converter with a new non-dissipative snubber is proposed that only requires two additional diodes, an inductor, and a multilayer ceramic capacitor. The main feature of the proposed snubber is that the components are not located on the main power processing path. This means they only require low ratings, improving cost-effectiveness. A prototype converter is analyzed, realized, and applied to a 300 W redundant DC-DC power supply module for a 5G cell base station. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. © 2024 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： Boost converter

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Retinal Blood Vessel Tracking and Diameter Estimation via Gaussian Process With Rider Optimization Algorithm

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IEEE Journal of Biomedical and Health Informatics 2024年第3期28卷 1173-1184页

作者： Ahmad, Nehal Lai, Kuan-Ting Tanveer, M. Indian Institute of Technology Indore Simrol Department of Mathematics Indore453552 India National Taipei University of Technology Department of International Program of Electrical Engineering and Computer Science Taipei106 Taiwan National Taipei University of Technology Department of Electronic Engineering Taipei106 Taiwan

Retinal blood vessels structure analysis is an important step in the detection of ocular diseases such as diabetic retinopathy and retinopathy of prematurity. Accurate tracking and estimation of retinal blood vessels in terms of their diameter remains a major challenge in retinal structure analysis. In this research, we develop a rider-based Gaussian approach for accurate tracking and diameter estimation of retinal blood vessels. The diameter and curvature of the blood vessel are assumed as the Gaussian processes. The features are determined for training the Gaussian process using Radon transform. The kernel hyperparameter of Gaussian processes is optimized using Rider Optimization Algorithm for evaluating the direction of the vessel. Multiple Gaussian processes are used for detecting the bifurcations and the difference in the prediction direction is quantified. The performance of the proposed Rider-based Gaussian process is evaluated with mean and standard deviation. Our method achieved high performance with the standard deviation of 0.2499 and mean average of 0.0147, which outperformed the state-of-the-art method by 6.32%. Although the proposed model outperformed the state-of-the-art method in normal blood vessels, in future research, one can include tortuous blood vessels of different retinopathy patients, which would be more challenging due to large angle variations. We used Rider-based Gaussian process for tracking blood vessels to obtain the diameter of retinal blood vessels, and the method performed well on the 'STrutred Analysis of the REtina (STARE) Database' accessed on Oct. 2020 (https://***/~ahoover/stare/). To the best of our knowledge, this experiment is one of the most recent analysis using this type of algorithm. © 2013 IEEE.

关键词： Blood vessels

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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