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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hao, Fengqi Zhu, Shulei Ma, Dexin Dong, Xiangjun Kong, Hoiio Liu, Xia Mu, Chunhua Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China City University of Macau Faculty of Data Science 999078 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China Communication College Qingdao Agricultural University Qingdao266109 China Maize Research Institute Shandong Academy of Agricultural Sciences National Engineering Laboratory of Wheat and Maize Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Jinan250100 China

ISBN: (纸本)9781665410205

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction - receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings. © 2024 IEEE.

关键词： Deep learning Maize seedling detection Object detection UAV

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Lightweight Deep Learning-Based network Traffic Classification Using Explainable Artificial Intelligence

Lightweight Deep Learning-Based Network Traffic Classificati...

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Computing (ICOCO), IEEE International Conference on

作者： Afif Haziq Haris Azizi Ariffin Faiz Zaki Nor Badrul Anuar Centre of Research for Cyber Security and Network (CSNET) Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

To address the growing demand for internet usage and connected devices, it is essential to implement robust network Traffic Classification (NTC) methods that guarantee network security and optimised resource utilisation. Deep learning approaches are continuously gaining traction due to their capabilities in learning complex network traffic patterns and effective classification. However, the performance of deep learning (DL) approaches comes with the cost of huge computational resources that struggle to perform under a resource-constrained environment. Therefore, this study proposes enhancing DL-based NTC by integrating an Explainable Artificial Intelligence (XAI) technique, the Shapley Additive exPlanations (SHAP), to identify and select the most important features. This study leverages the explainability of XAI to enhance model performance and optimise resource utilisation through feature selection. To evaluate the selected features, this paper compares three algorithms, which are Convolutional Neural networks (CNN), Multi-Layer Perceptron (MLP), and Gated Recurrent Units (GRU), using the ISCXVPN2016 dataset on an NVIDIA Tesla T4. The result shows a $\mathbf{1 2 . 4 2 \%}, \mathbf{1 0 . 7 2 \%}$, and $\mathbf{1 0 . 2 3 \%}$ decrease in RAM usage for MLP, CNN, and GRU, respectively, while reducing the GPU memory usage by $6 \%$ and an improvement up to $4.99 \%$ F1-score compared to the state-of-the-art.

关键词： Deep learning Performance evaluation Explainable AI Random access memory Graphics processing units Telecommunication traffic Traffic control network security Convolutional neural networks Resource management

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HPC-in-Containers: A Containerized Parallel Environment for Parallel Programming Learning Using Docker

HPC-in-Containers: A Containerized Parallel Environment for ...

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International Conference on Information Technology Systems and Innovation (ICITSI)

作者： Andria Arisal Heru Suhartanto Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia

ISBN: (数字)9798331511470

ISBN: (纸本)9798331511487

This paper presents HPC-in-Containers, a novel containerized parallel computing environment using Docker. It is designed to facilitate learning parallel programming concepts, where users do not have to deploy a multicomputer infrastructure. Instead, they can use their desktop and laptop to easily have a multicomputer environment by utilizing containerization on the multi-core CPU. The environment offers a comprehensive platform for exploring various parallel programming paradigms, including distributed memory parallelism, shared memory parallelism, and hybrid approaches. We include source code samples for each paradigm to demonstrate its capabilities: parallel multicomputer implementations using MPI, parallel thread implementations for single computers using OpenMP, and hybrid parallelization techniques combining both MPI and OpenMP. Furthermore, to enhance user interaction and ease of use, the Docker image incorporates a Jupyter Notebook interface with a C interpreter, allowing users to write, execute, and visualize parallel code through a web browser. This feature makes HPC-in-Containers particularly suitable for educational settings, enabling students to experiment with parallel programming concepts in a controlled, reproducible environment. Our work contributes to the field of parallel computing education by providing a flexible, scalable, and easily deployable platform that bridges the gap between theoretical concepts and practical implementation. It offers a valuable resource for educators, students, and researchers seeking to explore and master the intricacies of parallel programming in a containerized setting.

关键词： Visualization Technological innovation Portable computers Parallel programming Source coding Instruction sets Education Parallel processing User experience Monitoring

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Extending RIS Life Span for Reliable Communication Under Hardware Ageing Effects

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3395-3409页

作者： Mondal, Atiquzzaman Ghosh, Shyamal Singh, Keshav Biswas, Sudip Duong, Trung Q. Indian Institute of Information Technology Guwahati Department of Electrical Communication Engineering Guwahati781015 India Indian Institute of Science Education and Research Thiruvananthapuram Department of Data Science Thiruvananthapuram695551 India National Sun Yat-sen University Institute of Communications Engineering Kaohsiung80424 Taiwan Memorial University Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom

In this paper, we address the critical aspect of hardware ageing effects in reconfigurable intelligent surfaces (RISs), whereby the problem of extending the RIS's life cycle under the impact of non-residual stochastic hardware impairment is considered. Through the replication of the RIS hardware and diverse wireless environments within a statistical simulation environment, we formulate an electronic maintenance framework (EMF) for RIS, where we incorporate non-residual impairments through stochastic modeling and determine the electronic reliability of the system. Accordingly, we derive optimal analytical solutions within the EMF to determine whether systematic maintenance of the RIS hardware should be done immediately or postponed in order to extend the expected life cycle of the RIS system. Furthermore, for the scenario with imperfect maintenance of the RIS-aided system, a reliable communication framework (RCF) is also introduced with residual impairments to assess the error probability of the RIS-aided communication. The RCF is established by deriving the distribution of the received signal-to-interference-plus-noise ratio in the presence of residual hardware impairment arising due to imperfect maintenance of the RIS system. Extensive numerical examples are provided to elucidate the derived solutions and illustrate the reliability performance of the proposed framework under hardware impairments. © 2020 IEEE.

关键词： Wireless sensor networks

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment

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Procedia computer science 2024年 245卷 127-136页

作者： Christian Kenneth Rudi Nirwantono Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco -derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy.

关键词： bioinformatics network pharmacology Calamus draco colorectal cancer drug discovery

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data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Joko Pebrianto Trinugroho Alam Ahmad Hidayat Rudi Nirwantono Faisal Asadi Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment.

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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Private Synthetic Text Generation with Diffusion Models

arXiv

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arXiv 2024年

作者： Ochs, Sebastian Habernal, Ivan Trustworthy Human Language Technologies 1 Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

How capable are diffusion models of generating synthetics texts? Recent research shows their strengths, with performance reaching that of auto-regressive LLMs. But are they also good in generating synthetic data if the training was under differential privacy? Here the evidence is missing, yet the promises from private image generation look strong. In this paper we address this open question by extensive experiments. At the same time, we critically assess (and reimplement) previous works on synthetic private text generation with LLMs and reveal some unmet assumptions that might have led to violating the differential privacy guarantees. Our results partly contradict previous non-private findings and show that fully open-source LLMs outperform diffusion models in the privacy regime. Our complete source codes, datasets, and experimental setup are publicly available to foster future research1 © 2024, CC BY-SA.

关键词： Differential privacy

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Comparative Performance of Water Index for Water Segmentation Model Using U-Net Architecture

Comparative Performance of Water Index for Water Segmentatio...

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International Conference on computer, Control, Informatics and Its Applications (IC3INA)

作者： M. Athallah Dzikri Alhady Ira Maryati Roni Salambue Lia Sadita Andre Sihombing Aruni Yasmin Azizah Zul Indra Gita Sastria Department of Computer Science Universitas Riau Pekanbaru Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung Indonesia Faculty of Computer Science Universitas Indonesia (UI) Depok Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

Water segmentation has many benefits, such as for the hydrological cycle research, monitoring flood dynamics, and managing water resources. The U-Net is a famous segmentation architecture for water body segmentation. Water masks, used as labels in the U-Net architecture training process, are generated from water index classification. Since some water indices have been developed that claim beneficial performance, the comparative performance of each method has not yet been discovered. This study aims to compare the performance of several water index methods, including the Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Automated Water Extraction Index (AWEI), and Sentinel-2 Water Index (SWI). The study showed MNDWI obtained the best segmentation results and performance scores in urban areas with an IoU score of 0.97706. Meanwhile, SWI obtained the best segmentation results and performance scores in turbid water bodies with an IoU scores of 0.96075. This indicates that the water index is reliable in building a dataset for training the U-Net model. This study provides recommendations for selecting the appropriate water index that aligns with the characteristics of the segmented water bodies. These recommendations aim to enhance the accuracy and relevance of the water index in different environmental contexts.

关键词： Training Normalized difference water index Urban areas computer architecture Indexes Reliability Floods Informatics Water resources Monitoring

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