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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Zhijian Zheng Weilin Gai Peng Zhang Ming Zhou School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China TCA Institute of Software CAS CNCERT/CC School of Computer Science and Technology UCAS Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The Internet of Things (IoT) is increasingly vulnerable to security risks due to new network attacks. Deep learning-based intrusion detection systems (DL-IDS) have emerged as a key solution, but they face challenges like imbalanced datasets and lengthy training times in complex environments. While feature selection algorithms are commonly employed to mitigate these issues, mainstream methods can yield inconsistent results, failing to reflect data characteristics accurately and potentially introducing noise. To address this problem, we propose an ensemble stacking approach to combine multiple feature selection algorithms, thereby minimizing errors from individual approaches. Each feature selection method acts as a base learner to assess feature importance, while logistic regression is a meta-learner to integrate the outputs into a final result. Additionally, we developed a CNN-based intrusion detection model enhanced with BiLSTM and attention mechanisms to improve detection performance. Our approach was tested on the UNSW-NB15 and CIC-IDS2017 datasets, with results indicating a significant improvement in detection performance compared to using a single feature selection method.

关键词： Training Attention mechanisms Stacking Noise Intrusion detection Bidirectional long short term memory Feature extraction Robustness Internet of Things Security

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Freshness Classification of Hog Plum Fruit Using Deep Learning 4

Freshness Classification of Hog Plum Fruit Using Deep Learni...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Arunachalaeshwaran, V.R. Mahdi, Hussain Falih Choudhury, Tanupriya Sarkar, Tanmay Bhuyan, Bikram Pratim Informatics Cluster School of Computer Science Uttarakhand Dehradun248007 India University of Diyala Department of Computer and Software Engineering Baqubah Iraq Malda Polytechnic Department of Food Processing West Bengal Malda732102 India

ISBN: (纸本)9781665468350

Automatic freshness classification of fruits and veg-etables is an active area of research in the modern era. In order to maintain and preserve the grade quality of the fruits, the need to speed up processing is in a great urge. The fruit Hog Plum is noted to have high demand in Indian subcontinents. In this work we have utilized deep learning based techniques for performing freshness classification on hog Plum fruit. In this work, four convolutional neural network based models viz. ResNet18, MobileNetV2, MobileNetV3-Small, MobileNetV3-Large is pre-trained on the ImageNet dataset which were fine-tuned to perform binary classification on Hog Plum dataset cre-ated by us. The dataset was augmented for training purpose and hyperparameter tuning was performed using grid search and k-fold cross validation. The results of the model are then evaluated and compared in terms of several metrics like accuracy, precision, recall, Fl-Score, ROC-AUC Score and balanced accuracy score. Our results demonstrate that deep learning based techniques are effective for performing freshness classification of Hog Plum fruit. The models trained by us are lightweight in nature, hence are suitable to run on low-end and mobile devices increasing the applicability and adaptability of our proposed method. © 2022 IEEE.

关键词： Fruits

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Emerging Role of Industry 5.0 Digital Twins in Demand Response Electricity Market and Applications

Emerging Role of Industry 5.0 Digital Twins in Demand Respon...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Onile, Abiodun E. Belikov, Juri Petlenkov, Eduard Levron, Yoash Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Faculty of Electrical and Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel

ISBN: (纸本)9798350396782

Traditional power system is facing challenges demanding new operational requirements to meet targets of Net Zero Emissions by 2050. Aggregators are playing progressively important role in the demand response (DR) electricity market but are often riddled with deep level of market monopoly and lack of transparency/secrecy. Emerging real-time information technology (IT) applications and novel modelling of digital twins (DT) of individual electricity assets are challenging this position by promising improvements and openness that allows TSO direct access to available demand side flexibility. Additionally, DT technologies are facilitating how demand response services are delivered to end-users by allowing individual assets participation at the atomic level. In this study, the application potentials of assets’ DT in participating in the demand response electricity market was examined. Again, an overview of DT applications for DR was conducted. The novelty of this study is highlighted in development of new approach that facilitates individual end-user assets’ contribution to demand response efforts. The research identifies key useful questions that might serve as inspiration for stakeholders and policy-makers to further close existing gaps in the field of DT, smart-grid and demand response. © 2023 European Union.

关键词： Aggregators demand response digital twins Industry 5.0 smart-grid

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Towards a consensus of expected Student-Advisor responsibilities in engineering Capstone Projects 14

Towards a consensus of expected Student-Advisor responsibili...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Aljassmi, Hamad Philip, Babitha Ali, Luqman Krishnan, Salini United Arab Emirates University Department of Civil and Environmental Engineering Al Ain United Arab Emirates Department of Computer Science and Software Engineering United Arab Emirates University Al Ain United Arab Emirates United Arab Emirates University Department of Mechanical Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350399431

To deal with the complexity of today's engineering demands, leading universities intend to investigate educational advances and facilities to provide students a creative academic experience that integrates disciplinary knowledge and student transition to the world of the competitive job environment. By tackling a challenging design problem, the capstone project (CP) harnesses creative learning and transfer hands-on abilities into the industrial environment. Effective coaching on the understanding of course objectives aids both advisors and students to properly plan their tasks in CP design. Students and advisors must have a shared understanding of their roles and responsibilities, as well as the extent of independence anticipated from students. This study identifies specific areas in the capstone project that require direct advisor mentoring, aside with other areas in which students are anticipated to work independently, through a questionnaire survey consisting of 206 participants from both segments: advisors and final year engineering students. The survey results outline the discrepancies and consensus of perceptions amongst both segments, provide insights about current CP delivery, and highlight potential modifications that may be considered for improving the delivery of the capstone project where optimum levels of knowledge and skills are obtained by students. The results of this study serve as basis for guiding faculty members about finding the right balance of mentorship and students' independence, as students progress throughout different stages of their final year design capstone projects. © 2023 IEEE.

关键词： Students

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Chromosome Feature Extraction and Ideogram-Powered Chromosome Categorization

Chromosome Feature Extraction and Ideogram-Powered Chromosom...

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作者： Pysarchuk, Oleksii Mironov, Yurii Faculty of Informatics and Computer Science National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kiev03056 Ukraine Faculty of Cybersecurity Computer and Software Engineering National Aviation University Kiev03058 Ukraine

Chromosomal diseases diagnostics is based on identifying chromosomes and detecting abnormalities in them. It is a sophisticated procedure that is performed manually or partially automated, therefore is prone to human error. Automation of such diagnostics is a complex multistage task that presents many challenges. One of such challenges is chromosome feature extraction and classification. Categorizing chromosomes is necessary for determining a diagnosis. This problem has been previously covered by research papers, but there is no complete solution by now. Moreover, the vast majority of proposals rely on Machine Learning (ML) and Neural Networks (NN). Usage of ML-powered solutions may pose a problem due to difficulty of dataset collection - medical data may be diverse, heterogeneous and hard to collect because of its sensitive nature. Given paper presents a proposal of chromosome feature extraction and categorization without NN. It uses computer Vision (CV) techniques for chromosome feature extraction: medial axis is extracted from a chromosome to identify its direction, and than chromosome bands (colored segments that make up distinct chromosome pattern) are identified along the axis. Having transformed chromosome image into a discrete piece of data, it is passed to a proposed multiple-criteria decision-making algorithm. This algorithm is designed to categorize chromosomes without learning dataset, instead making use of ideogram (schematic reference chromosome) data. The algorithm is focused on processing a chromosome as a set of segments, where each segment can be recognized by a special predicate. A predefined combination of such predicates should allow recognizing chromosome type. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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Prior Information Guided Coarse-to-fine Dual-branch Encoding Network for Fovea Localization and Optic Disc/Cup Segmentation

Prior Information Guided Coarse-to-fine Dual-branch Encoding...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haijun Lei Hai Xie Jia Zhao Danrui Zhao Limin Huang Baiying Lei College of Computer Science & Software Engineering Shenzhen University Shenzhen China School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Information & Science Southwest University Shenzhen China

Fundus images are commonly used to document the presence and severity of various retinal degenerative diseases, where the fovea, optic disc (OD), and optic cup (OC) serve as important anatomical landmarks. Locating and segmenting these landmarks are crucial for clinical diagnosis and treatment. Many existing methods treat the recognition of the fovea, OD, and OC as separate tasks without incorporating any clinical prior knowledge related to various anatomical structures. In this paper, we propose a prior information guided coarse-to-fine dual-branch encoding network, which enables fovea localization and OD/OC segmentation. In coarse stage, we employ a dual-branch network consisting of convolutional neural network (CNN) and Transformer to encode local and global features, and then utilize multi-scale feature fusion techniques to merge the extracted semantic features, aiming to enhance the localization accuracy. In addition, we effectively use the distance information from each pixel to the landmark of interest, and output the results of distance map and heat map regression as prior information to further guide the network to learn the positional relationship between fovea and OD. In fine stage, we refine the region of interest (ROI) of the OD, balance the distribution of the OD and OC using polar coordinate transformation (PCT), extract critical boundary features using the boundary attention module (BAM), and improve the generalization performance of our method through model ensemble strategy. Extensive experimental results demonstrate that our proposed method outperforms existing state-of-the-art (SOTA) methods on the publicly available GAMMA and REFUGE datasets.

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RepEPnP: Weakly Supervised 3D Human Pose Estimation with EPnP Algorithm

RepEPnP: Weakly Supervised 3D Human Pose Estimation with EPn...

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International Joint Conference on Neural Networks (IJCNN)

作者： Huaijing Lai Zhenhua Tang Xiaoyan Zhang College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer and Information Hefei University of Technology Hefei China

This paper describes an end-to-end weakly super-vised framework for estimating 3D human pose from a single image. The model is trained by projecting 3D pose to 2D pose for matching ground-truth 2D pose for supervision. To obtain accu-rate projection from 3D pose to 2D pose, a mathematical camera model based on intrinsic and extrinsic camera parameters is used. Specifically, we use EPnP algorithm to estimate extrinsic transformation matrix to transform the estimated 3D pose to be reprojected back to 2D pose. The advantage of this projection is that it requires no training and it is robust to the diversity of training datasets. We further constrain the pose generation using an adversarial generative network, where a transformer is used as the 3D pose generator. Transformer can use self-attention mechanism to establish dependencies between each joint and predict pose based on important joints. Based on our reprojection method, our method achieves competitive results on Human3.6M and MPI-INF-3DHP among weakly supervised methods. The experiments also demonstrate our model's generalization ability for wild images.

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AlexNet based Facial Expression Classification

AlexNet based Facial Expression Classification

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2022 International Conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2022

作者： Tureke, Yusufu Xu, Wenshuo Zhao, Jikang Computer Science and Technology Donghua University shanghai shanghai201620 China Software Engineering Chengdu Jincheng College Jincheng College of Sichuan University SiChuan Chengdu610097 China Computer Science and Technology Shandong University of Technology Shandong Zibo255000 China

ISBN: (纸本)9781510655867

In this paper, we use the image data in the dataset-FER2013 as a sample. First, we format the CSV file in the dataset. After finding that there is a large difference in the number of different types of pictures, in order to compare and verify the impact of the number difference on the results, we enhanced the data set through the proposed data augmentation method, so that the data set can meet the experimental needs. After that, by understanding the artificial intelligence learning algorithms at home and abroad, we decided to establish a training model using AlexNet network, and divided the pictures to be identified into three groups with different numbers: original data set, down sampling data set and dataset with data augmentation. In the experiment, we achieves 52.3% accuracy after data enhancement, and 66.9% accuracy using the down sampling dataset. © 2022 SPIE.

关键词： Signal sampling

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Phase Re-Service in Reinforcement Learning Traffic Signal Control

Phase Re-Service in Reinforcement Learning Traffic Signal Co...

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International Conference on Intelligent Transportation

作者： Zhiyao Zhang George Gunter Marcos Quinones-Grueiro Yuhang Zhang William Barbour Gautam Biswas Daniel Work Department of Civil and Environmental Engineering Institute for Software Integrated Systems Vanderbilt University USA Institute for Software Integrated Systems Vanderbilt University USA Department of Computer Science Institute for Software Integrated Systems Vanderbilt University USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation.

关键词： Shock waves Reinforcement learning Delays Logic Standards Queueing analysis Optimization Intelligent transportation systems

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Leader-following impulsive consensus of third-order nonlinear multi-agent systems with delay

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Physica A: Statistical Mechanics and its Applications 2025年 672卷

作者： Zhang, Qunjiao Tong, Ping Wu, Xiaoqun Liu, Jie Wan, Li School of Mathematical and Physical Sciences Research Center of Nonlinear Science Wuhan Textile University Hubei Wuhan430073 China College of Computer Science and Software Engineering Shen Zhen University Shen Zhen518060 China

This paper addresses the consensus problem in third-order nonlinear multi-agent systems with delay. Based on a strict comparison theorem and the average impulsive interval method, some sufficient criteria for achieving impulsive consensus are derived, which are formulated through two constraints involving appropriate feedback controllers and impulsive control law. Finally, some numerical simulations provided in the example demonstrate the validity of the proposed impulsive consensus criteria. © 2025

关键词： Feedback control

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