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2023 International Conference on Computer science, Information technology and engineering, ICCoSITE 2023

作者： Munir, Sirojul Seminar, Kudang Boro Sudradjat Sukoco, Heru Bogor Agricultural University Departemen of Computer Science Bogor Indonesia Nurul Fikri College of Technology Departemen of Informatics Depok Indonesia Bogor Agricultural University Departement of Mechanical and Biosystem Engineering Bogor Indonesia Bogor Agricultural University Department of Agronomy and Horticulture Bogor Indonesia Bogor Agricultural University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9798350320954

One of the laborious tasks in operating and managing oil palm lands is measuring the nutrient content of leaves in a timely and periodic manner. It takes a manual process and takes a long time to get leaf samples that are representative of the observed oil palm area, especially in peat soil area. In addition, laboratory analysis of leaf samples is expensive and introduces creates significant time delays for queuing. This paper discusses methods for measuring leaf nitrogen levels in a timely manner based on imagery obtained by C-SAR Sentinel-1A using smart and precision agricultural approaches and applications. The intelligent computational model uses Random Forest Regression to calculate leaf nitrogen content based on spatial image processing and analysis. The calculated leaf nitrogen content is then used to examine and identify the variability of nitrogen content in a given oil palm area. Identification of nutrient variability is one of the approaches and goals of precision agriculture. Our computation model was developed using 553 leaf sampling units (LSU) and validated using 10-Fold validation method, resulting in correctness, MAPE, RMSE and R2 of 95.69%, 4.31%, 0.1119 and 93.28% respectively. The validated model was tested using a set of new data, which is not in the data samples for model development and validation, was resulting in correctness, MAPE, RMSE and R2 of 89.91%, 10.11%, 0.284, and 53.89% respectively. The visual maps can be produced and displayed to visualize variability of nitrogen contents in a peat soil area. This improves visibility and transparency in monitoring nutrient in timely and periodic manner. The future use of the proposed method is to support the application of smart and precision agriculture practices to recommend fertilizers types and dozes for oil palm in peat soil areas based on the variability of leaf nutrient contents. © 2023 IEEE.

关键词： Intelligent systems

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Cross-Modality Perturbation Synergy Attack for Person Re-identification 38

Cross-Modality Perturbation Synergy Attack for Person Re-ide...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

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How we can use text classification in the Back-Office environment of a bank as 'business as usual' solution 17

How we can use text classification in the Back-Office enviro...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Krutilla, Zsolt Kovari, Attila Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary Informatics Institution University of Dunaújváros Dunaújváros Hungary Institute of Engineering Alba Regia Technical Faculty buda University Budapest Hungary Institute of Digital Technology Faculty of Informatics Eszterházy Károly Catholic University Eger Hungary University of Dunaújváros Institute of Engineering Department of Natural Science Dunaújváros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary

ISBN: (纸本)9798350321104

Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and technology until it can be used reliably for business purposes. Transformer models, such as GPT or BERT, are currently showing outstanding results in the field of natural-language processing, but they require huge computational power and data to teach, but these conditions can only be met by larger research centers and large companies, and their inaccuracy makes them unsuitable for use as a 'business as usual' (BAU) solution. In this paper, we present a solution that is able to overcome these problems by focusing on accuracy and usability, and that can also bring a new perspective to the process of teaching deep learning models. © 2023 IEEE.

关键词： Information retrieval systems

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Development and demonstration of a prototype system for the early detection of drowning

Development and demonstration of a prototype system for the ...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Konishi, Nagato Ishigaki, Yo Nakada, Tsubasa Nemoto, Wataru Iinuma, Seizi Hoshino, Taisuke Ohkawara, Kazunori University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan Chief Executive Officer Plan I Corporation University of Electro-Communications Bioscience and Technology Program Department of Engineering Science Tokyo Japan

ISBN: (纸本)9781665402064

We have developed a small device that can measure 3-axis acceleration and pressure underwater, and collected data on drowning reactions by asking participants to reproduce the characteristic behavior of drowning. The data collected in this study is expected to aid the early detection of drowning reactions and reduce the number of accidents caused by drowning. © 2022 IEEE.

关键词： Accidents

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User Engagement with the User Manuals of Smart Medical Wearable Technologies and Relevant Questions of User Behavior

User Engagement with the User Manuals of Smart Medical Weara...

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34th Medical informatics Europe Conference, MIE 2024

作者： Simon, Aniko Xinyu, Qiu Maksimovic, Ana Talovics, Melinda Baranyai-Santa, Eszter Berze, David Odor, Viktoria Kara, Peter A. Sigma Technology Közraktár u. 30-32 Budapest1093 Hungary Department of Electrical Engineering The Fu Foundation School of Engineering and Applied Science Columbia University 2960 Broadway New YorkNY10027-6902 United States University of Donja Gorica Oktoih 1 Podgorica81000 Montenegro Department of Networked Systems and Services Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. Budapest1111 Hungary

ISBN: (纸本)9781643685335

In this paper, engagement with smart medical wearables and with their user manuals, as well as related user behavior are studied. A research questionnaire containing 15 single-choice questions was completed by 1381 test participants to address relevant topics of the investigated area, including trust in measured medical data, device calibration, technical terminologies and function discovery in the documentation, information sources beyond the documentation, and wearing such devices to bed. The questionnaire particularly focused on device functionalities and characteristics that initially led to the purchase of the smart medical wearable. © 2024 The Authors.

关键词： Electronic health record

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Risk-Aware Reinforcement Learning Based Federated Learning Framework for Io V

Risk-Aware Reinforcement Learning Based Federated Learning F...

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IEEE Conference on Wireless Communications and Networking

作者： Yuhan Chen Zhibo Liu Xiaozhen Lu Liang Xiao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Informatics and Communication Engineering Xiamen University Xiamen China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Federated learning helps protect data privacy for Internet of vehicles (Io V) by selecting a number of participated nodes but suffers from performance degradation such as low model training accuracy in the highly dynamic and large-scale Io V systems under selfish attacks. In this paper, we propose a risk-aware reinforcement learning based federated learning framework against selfish attacks for Io V,which jointly optimizes the training policy (i.e., the selection of participated vehicles and the corresponding local training data size) based on the state including the global model training accuracy, local model quality, training latency, data rate, and participation rate. By designing a punishment function to evaluate the immediate risk of each choosing training policy, this scheme avoids risky policies that result in extremely low training accuracy and high training latency to satisfy the requirements of local tasks such as the quality of service requirements. An evaluated neural network involved fully connected layers is designed to fast extract the global and local training features and thus accelerate the convergence speed. Experimental results based on both the MNIST and CIFAR-10 datasets verify that our scheme outperforms the benchmarks with higher training accuracy and less training latency.

关键词： Training Accuracy Federated learning Training data Reinforcement learning Benchmark testing Feature extraction

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Integrating Simulation and Optimization: A Case Study in Pamplona for Self-Collection Delivery Points Network Design 35

Integrating Simulation and Optimization: A Case Study in Pam...

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35th European Modeling and Simulation Symposium, EMSS 2023

作者： Izco, Irene Serrano-Hernandez, Adrian Sawik, Bartosz Faulin, Javier GILT-OR Group Institute of Smart Cities Dept. Statitistics Computer Science and Mathematics Public University of Navarra Campus Arrosadia s/n Pamplona31006 Spain Department of Business Informatics and Engineering Management AGH University of Science and Technology Mickiewicza 30 Kraków30059 Poland

ISBN: (纸本)9788885741881

The disruptions experienced by the processes in the last mile delivery during the SARS-CoV-2 pandemic raised the dilemma of up-to-date last mile approaches for Urban Logistics (UL) issues. Self-Collection Delivery Systems (SCDS) have been proved to be an improvement for all the players of the SC, providing flexibility of time-windows and reducing overall mileage, delivery time and, consequently, gas emissions. Differing from previous works involving hybrid modeling for automated parcel lockers (APL) network design, this paper brings a System Dynamics Simulation Model (SDSM) to forecast online shopping demand in the Spanish city of Pamplona. A bi-criteria Facility Location Problem (FLP) is solved by means of an e-constraint method, where e is defined as the level of coverage of the total demand. The experiment run considers 90% of demand coverage, in order to obtain the most complex network possible. The simulation and demand forecast was carried out using Anylogic simulation software and the optimization procedure makes use of the Java-based CPLEX API solver. © 2023 The Authors.

关键词： Complex networks

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EffNet-CNN:A Semantic Model for Image Mining&Content-Based Image Retrieval

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Computer Modeling in engineering & sciences 2025年第5期143卷 1971-2000页

作者： Rajendran Thanikachalam Anandhavalli Muniasamy Ashwag Alasmari Rajendran Thavasimuthu Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Informatics and Computer Systems College of Computer ScienceKing Khalid University Abha61421Saudi Arabia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia Department of Sustainable Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai602105India

Content-Based Image Retrieval(CBIR)and image mining are becoming more important study fields in computer vision due to their wide range of applications in healthcare,security,and various *** image retrieval system mainly relies on the efficiency and accuracy of the classification *** research addresses the challenge of enhancing the image retrieval system by developing a novel approach,EfficientNet-Convolutional Neural Network(EffNet-CNN).The key objective of this research is to evaluate the proposed EffNet-CNN model’s performance in image classification,image mining,and *** novelty of the proposed EffNet-CNN model includes the integration of different techniques and *** model includes the Mahalanobis distance metric for feature matching,which enhances the similarity *** model extends EfficientNet architecture by incorporating additional convolutional layers,batch normalization,dropout,and pooling layers for improved hierarchical feature extraction.A systematic hyperparameter optimization using SGD,performance evaluation with three datasets,and data normalization for improving feature *** EffNet-CNN is assessed utilizing precision,accuracy,F-measure,and recall metrics across MS-COCO,CIFAR-10 and 100 *** model achieved accuracy values ranging from 90.60%to 95.90%for the MS-COCO dataset,96.8%to 98.3%for the CIFAR-10 dataset and 92.9%to 98.6%for the CIFAR-100 dataset.A validation of the EffNet-CNN model’s results with other models reveals the proposed model’s superior *** results highlight the potential of the EffNet-CNN model proposed for image classification and its usefulness in image mining and CBIR.

关键词： Image mining CBIR semantic features EffNet-CNN image retrieval

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Latest Innovations in Intelligent Network-on-Chip Architectures: A Systematic Review

Latest Innovations in Intelligent Network-on-Chip Architectu...

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International Workshop on Network on Chip Architectures, NoCArc

作者： Elena Kakoulli Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798331506421

ISBN: (纸本)9798331506438

This review investigates the latest advancements in intelligent Network-on-Chip (NoC) architectures, focusing on innovations from 2022 to 2024. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into NoC designs has driven significant improvements in performance, scalability, energy efficiency, and security. Key contributions include AI/ML-driven techniques to optimize routing, reduce latency, manage power consumption, and enhancing security against emerging threats. Despite these advancements, challenges such as computational overhead and integration complexities remain. This review consolidates these findings, addresses existing challenges, and proposes future research directions to further leverage AI and ML for developing robust and adaptable NoC architectures.

关键词： Technological innovation Scalability Power system management Computer architecture Network-on-chip Predictive models Routing Energy efficiency Complexity theory Security

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Evaluating Simple and Complex Models' Performance When Predicting Accepted Answers on Stack Overflow 48

Evaluating Simple and Complex Models' Performance When Predi...

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48th Euromicro Conference on Software engineering and Advanced Applications, SEAA 2022

作者： Omondiagbe, Osayande P. Licorish, Sherlock A. Macdonell, Stephen G. Landcare Research Department of Informatics Lincoln New Zealand University of Otago Department of Information Science Dunedin New Zealand Auckland University of Technology Software Engineering Research Lab Auckland New Zealand

ISBN: (纸本)9781665461528

Stack Overflow is used to solve programming issues during software development. Research efforts have looked to identify relevant content on this platform. In particular, researchers have proposed various modelling techniques to predict acceptable Stack Overflow answers. Less interest, however, has been dedicated to examining the performance and quality of typically used modelling methods with respect to the model and feature complexity. Such insights could be of practical significance to the many practitioners who develop models for Stack Overflow. This study examines the performance and quality of two modelling methods, of varying degree of complexity, used for predicting Java and JavaScript acceptable answers on Stack Overflow. Our dataset comprised 249,588 posts drawn from years 2014-2016. Outcomes reveal significant differences in models' performances and quality given the type of features and complexity of models used. Researchers examining model performance and quality and feature complexity may leverage these findings in selecting suitable modelling approaches for Q&A prediction. © 2022 IEEE.

关键词： Forecasting

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